Methods for physiological monitoring, training, exercise and regulation

ABSTRACT

Computer executable software and device for guiding brain activity training comprising: logic which takes data corresponding to activity measurements of one or more internal voxels of a brain and determines one or more members of the group consisting of: a) what next stimulus to communicate to the subject, b) what next behavior to instruct the subject to perform, c) when a subject is to be exposed to a next stimulus, d) when the subject is to perform a next behavior, e) one or more activity metrics computed from the measured activity, f) a spatial pattern computed from the measured activity, g) a location of a region of interest computed from the measured activity, h) performance targets that a subject is to achieve computed from the measured activity, i) a performance measure of a subject&#39;s success computed from the measured activity, j) a subject&#39;s position relative to an activity measurement instrument; and logic for communicating information based on the determinations to the subject in substantially real time relative to when the activity is measured.

RELATED APPLICATION

[0001] This application is a continuation in part of Provisional U.S.Patent Application No. 60/265,204, filed Jan. 30, 2001; Provisional U.S.Patent Application No. 60/265,214, filed Jan. 30,2001; and ProvisionalU.S. Patent Application No. <“Methods for Phyisological Monitoring,Training and Regulation”>, filed Nov. 2, 2001, each of which areincorporated herein in their entirety.

FIELD OF THE INVENTION

[0002] The present invention relates to methods, software and systemsfor monitoring physiological activity, particularly in the human brainand nervous system and therapeutic applications relating thereto.

DESCRIPTION OF RELATED ART

[0003] A variety of different brain scanning methodologies have beendeveloped that may be used to identify changes of mental states orconditions including Positron Emission Tomography (PET) and SinglePhoton Emission Computed Tomography (SPECT), electroencephalogram (EEG)based imaging, magnetoencephalogram (MEG) based imaging, and functionalmagnetic resonance imaging (fMRI).

[0004] For example, magnetic resonance imaging (MRI) has been usedsuccessfully to study blood flow in vivo. U.S. Pat. Nos.4,983,917,4,993,414, 5,195,524, 5,243,283, 5,281,916, and 5,227,725provide examples of the techniques that have been employed. Thesepatents are generally related to measuring blood flow with or withoutthe use of a contrast bolus, some of these techniques referred to in theart as MRI angiography. Many such techniques are directed to measuringthe signal from moving moieties (e.g., the signal from arterial bloodwater) in the vascular compartment, not from stationary tissue. Thus,images are based directly on water flowing in the arteries, for example.U.S. Pat. No. 5,184,074, describes a method for the presentation of MRIimages to the physician during a scan, or to the subject undergoing MRIscanning.

[0005] In the brain, several researchers have studied perfusion bydynamic MR imaging using an intravenous bolus administration of acontrast agent in both humans and animal models (See, A. Villringer etal, Magn. Reson, Med., Vol. 6 (1988), pp 164-174; B. R. Rosen et al,Magn. Reson. Med., Vol. 14 (1999), pp. 249-265; J. W. Belliveau et al,Science, Vol. 254 (1990), page 716). These methods are based on thesusceptibility induced signal losses upon the passage of the contrastagent through the microvasculature. Although these methods do notmeasure perfusion (or cerebral blood flow, CBF) in classical units, theyallow for evaluation of the related variable rCBV (relative cerebralblood volume). For example, in U.S. Pat. No. 5,190,744 to Rocklage,quantitative detection of blood flow abnormalities is based on the rate,degree, duration, and magnitude of signal intensity loss which takesplace for a region following MR contrast agent administration asmeasured in a rapid sequence of magnetic resonance images.

[0006] With the advent of these brain scanning methodologies, blood flowin various brain areas has been effectively correlated with variousbrain disorders such as Attention Deficit Disorder (ADD), Schizophrenia,Parkinson's Disease, Dementia, Alzheimers Disease, EndogenousDepression, Oppositional Defiant Disorder, Bipolar Disorder, memoryloss, brain trauma, Epilepsy and others.

[0007] The prior art also describes a variety of inventions dating backto the 1960's have provided a way allowing subjects to learn to controlmuscle, autonomic or neural activity through processes. Examples anddescriptions are included in U.S. Pat. No. 4,919,143. U.S. Pat. No.4,919,143, U.S. Pat. No. 5,406,957, U.S. Pat. No. 5,899,867 and U.S.Pat. No. 6,097,981.

[0008] Considerable research has also been directed to biologicalfeedback of brainwave signals known as electroencephalogram (EEG)signals. One conventional neurophysiological study established afunctional relationship between behavior and bandwidths in the 12-15 Hzrange relating to sensorimotor cortex rhythm EEG activity (SMR).Sterman, M. B., Lopresti, R. W., & Fairchild, M. D. (1969).Electroencephalographic and behavioral studies of monomethylhdrazinetoxicity in the cat. Technical Report AMRL-TR-69 3, Wright-Patterson AirForce Base, Ohio, Air Systems Command. A cat's ability to maintainmuscular calm, explosively execute precise, complex and coordinatedsequences of movements and return to a state of calm was studied bymonitoring a 14 cycle brainwave. The brainwave was determined to bedirectly responsible for the suppression of muscular tension and spasm.It was also demonstrated that the cats could be trained to increase thestrength of specific brainwave patterns associated with suppression ofmuscular tension and spasm. Thereafter, when the cats were administereddrugs which would induce spasms, the cats that were trained tostrengthen their brainwaves were resistent to the drugs.

[0009] The 12-15 Hz SMR brainwave band has been used in EEG training forrectifying pathological brain underactivation. In particular thefollowing disorders have been treated using this type of training:epilepsy (as exemplified in M. B. Sterman's, M. B. 1973 work on the“Neurophysiologic and Clinical Studies of Sensorimotor EEG BiofeedbackTraining: Some Effects on Epilepsy” L. Birk (Ed.), Biofeedback:Behavioral Medicine, New York: Grune and Stratton); Giles de laTourette's syndrome and muscle tics (as exemplified in the inventor's1986 work on “A Simple and a Complex Tic (Giles de la Tourette'sSyndrome): Their response to EEG Sensorimotor Rhythm BiofeedbackTraining”, International Journal of Psychophysiology, 4, 91 97 (1986));hyperactivity (described by M. N. Shouse, & J. F. Lubar's in the workentitled “Operant Conditioning of EEG Rhythms and Ritalin in theTreatment of Hyperkinesis”, Biofeedback and Self-Regulation, 4, 299-312(1979); reading disorders (described by M. A. Tansey, & Bruner, R. L.'sin “EMG and EEG Biofeedback Training in the Treatment of a 10-year oldHyperactive Boy with a Developmental Reading Disorder”, Biofeedback andSelf-Regulation, 8, 25-37 (1983)); learning disabilities related to thefinding of consistent patterns for amplitudes of various brainwaves(described in Lubar, Bianchini, Calhoun, Lambert, Brody & Shabsin's workentitled “Spectral Analysis of EEG Differences Between Children with andwithout Learning Disabilities”, Journal of Learning Disabilities, 18,403-408 (1985)) and; learning disabilities (described by M. A. Tansey in“Brainwave signatures-An Index Reflective of the Brain's FunctionalNeuroanatomy: Further Findings on the Effect of EEG Sensorimotor RhythmBiofeedback Training on the Neurologic Precursors of LearningDisabilities”, International Journal of Psychophysiology, 3, 85-89(1985)). In sum, a wide variety of disorders, whose symptomologyincludes impaired voluntary control of one's own muscles and a loweredcerebral threshold of overload under stress, were found to be treatableby “exercising” the supplementary and sensorimotor areas of the brainusing EEG biofeedback.

[0010] U.S. Pat. No. 5,995,857 describes an apparatus and method forproviding biofeedback of human central nervous system activity usingradiation detection. In this patent, radiation from the brain resultingeither from an ingested or injected radioactive material or radiofrequency excitation or light from an external source impinging on thebrain is measured by suitable means and is made available to the subjecton which the measurement is being made for his voluntary control. Themeasurement may be metabolic products of brain activity or some qualityof the blood, such as its oxygen content. The system described thereinutilizes red and infrared light to illuminate the brain through thetranslucent skull and scalp.

SUMMARY OF THE INVENTION

[0011] The present invention is directed to various methods relating tothe use of behaviors performed by a subject and/or perceptions made by asubject that alter the activity of one or more brain regions ofinterest. It should be recognized that this alteration in activation maybe a decrease or increase in activity at the different regions ofinterest.

[0012] One particular aspect of the invention relates to the use ofbehaviors performed by a subject and/or perceptions made by a subjectthat alter the activity of one or more regions of interest incombination with measuring the activation of the one or more regions ofinterest. Preferably, the measurement is performed in substantially realtime relative to the behavior or perception. Activation metrics may becalculated based on the measured activity and used to monitor changes inactivation.

[0013] Another particular aspect of the invention relates to thecommunication of information to a subject in combination with measuringthe activation of the one or more regions of interest of the subjectwhere the what, when, and/or how the information is communicated isdetermined, at least partially, based on the measured activity.Preferably, activity measurements are made continuously so that what,when, and/or how information is communicated to a subject in view of theactivity measurements can be continuously determined. Examples of typesof information that may be controlled in this manner include, but arenot limited to instructions, stimuli, physiological measurement relatedinformation, and subject performance related information.

[0014] The present invention also relates to software that is designedto perform one or more operations employed in combination with themethods of the present invention. The various operations that are or maybe performed by software will be understood by one of ordinary skill, inview of the teaching provided herein.

[0015] The present invention also relates to systems that may be used incombination with performing the various methods according to the presentinvention. These systems may include a brain activity measurementapparatus, such as a magnetic resonance imaging scanner, one or moreprocessors and software according to the present invention. Thesesystems may also include mechanisms for communicating information suchas instructions, stimulus information, physiological measurement relatedinformation, and/or subject performance related information to thesubject or an operator. Such communication mechanisms may include adisplay, preferably a display adapted to be viewable by the subjectwhile brain activity measurements are being taken. The communicationmechanisms may also include mechanisms for delivering audio, tactile,temperature, or proprioceptive information to the subject. In someinstances, the systems further include a mechanism by which the subjectmay input information to the system, preferably while brain activitymeasurements are being taken.

[0016] In one embodiment, a method is provided for selecting how toachieve activation of one or more regions of interest of a subject, themethod comprising: evaluating a set of behaviors that a subjectseparately performs regarding how well each of the behaviors in the setactivate the one or more regions of interest; and selecting a subset ofthe behaviors from the set found to be effective in activating the oneor more regions of interest. In one variation, evaluating the set ofbehaviors comprises calculating and comparing activation metricscomputed for each behavior based on measured activities for thedifferent behaviors. In one variation, the behaviors evaluated are overtbehaviors involving a physical motion of the body of the subject. Inanother variation, the behaviors are covert behaviors only cognitiveprocesses which do not lead to a physical motion of the body of thesubject.

[0017] In another embodiment, a method is provided for selecting how toachieve activation of one or more regions of interest of a subject, themethod comprising: evaluating a set of stimuli that a subject isseparately exposed to regarding how well each of the different stimulicause the subject to have a perception that activates the one or moreregions of interest; and selecting a subset of the stimuli from the setfound to be effective in causing activation of the one or more regionsof interest. In one variation, evaluating the set of stimuli comprisescalculating and comparing activation metrics computed for each stimulibased on measured activities for the different stimuli.

[0018] In another embodiment, a method is provided, the methodcomprising: evaluating a set of perceptions that a subject may haveregarding how well each of the perceptions activate the one or moreregions of interest; and selecting a subset of the perceptions from theset found to be effective causing activation of the one or more regionsof interest. In one variation, evaluating the set of perceptionscomprises calculating and comparing activation metrics computed for eachstimuli based on measured activities for the different perceptions.

[0019] In another embodiment, computer executable logic is provided forselecting how to achieve activation of one or more regions of interestof a subject, the software comprising: logic for calculating activationmetrics for activity measured for one or more regions of interest; andlogic for comparing a set of calculated activation metrics and selectinga subset of the activation metrics having a superior activation of theone or more regions of interest.

[0020] In another embodiment, computer executable logic is provided forselecting how to achieve activation of one or more regions of interestof a subject, the software comprising: logic for calculating activationmetrics for activity measured for one or more regions of interest duringfor a plurality of different behaviors; and logic for comparing thecalculated activation metrics for the plurality of behaviors andselecting behaviors from the plurality based on the comparison ofactivation metrics.

[0021] In another embodiment, a method is provided for selecting abehavior for causing activation of one or more regions of interest of asubject, the method comprising: employing computer executable logic toselect in substantially real time a next behavior for a subject toperform during training based, at least in part, on activitymeasurements made at or before the time the selection is made.

[0022] In another embodiment, a method is provided for directingbehavior, the method comprising: employing computer executable logic toselect in substantially real time a next behavior for a subject toperform during training based, at least in part, on activitymeasurements made at or before the time the selection is made.

[0023] In another embodiment, a method is provided for selecting abehavior for causing activation of one or more regions of interest of asubject, the method comprising: employing computer executable logic toselect a next behavior for a subject to perform during training based,at least in part, on one or more behaviors previously used duringtraining. In a variation, the selection is based on a combination of theone or more behaviors previously used during training and the activitymeasurements associated with the behaviors.

[0024] In another embodiment, a method is provided for selecting abehavior for causing activation of one or more regions of interest of asubject, the method comprising: employing computer executable logic toselect a next behavior for a subject to perform during training based,at least in part, on measured activities of one or more regions ofinterest in response to the performance of one or more earlierbehaviors. In a variation, the selection is based on a combination ofthe measured activity and the identity of the one or more earlierbehaviors. It is noted that the computer executable logic may optionallycompute activity metrics from the measured activity for the one or moreearlier behaviors and base the selection on the activity metrics.Optionally, the computed activity metrics are based on a comparison witha rest state.

[0025] In another embodiment, a method is provided for selecting astimulus for causing activation of one or more regions of interest of asubject, the method comprising: employing computer executable logic toselect in substantially real time a next stimulus to communicate to asubject during training based, at least in part, on activitymeasurements made at the time the selection is made.

[0026] In another embodiment, a method is provided for selecting astimulus for causing activation of one or more regions of interest of asubject, the method comprising: employing computer executable logic toselect a next stimulus to communicate to a subject during trainingbased, at least in part, on one or more stimuli previously communicatedduring training. In a variation, the selection is based on a combinationof the one or more stimuli previously communicated and the activitymeasurements associated with the stimuli.

[0027] In another embodiment, a method is provided for selecting astimulus for causing activation of one or more regions of interest of asubject, the method comprising: employing computer executable logic toselect a next stimulus to communicate to a subject during trainingbased, at least in part, on measured activities of one or more regionsof interest in response to the communication of one or more earlierstimuli. In a variation, the selection is based on a combination of themeasured activity and the identity of the one or more earlier stimuli.It is also noted that the computer executable logic may optionallycompute activity metrics from the measured activity for the one or moreearlier stimuli and base the selection on the activity metrics.Optionally, the computed activity metrics are based on a comparison witha rest state.

[0028] In regard to the above embodiments, it is noted that the nextbehavior or stimulus that is selected may be the same or different thanthe one or more earlier behaviors or stimuli. In another embodiment, acomputer assisted method is provided for guiding brain activity trainingcomprising: measuring activity of one or more regions of interest of asubject; employing computer executable logic to select a behavior orstimulus for activating the one or more regions of interest based, atleast in part, on the measured brain activity; and employing computerexecutable logic to communicate the selected behavior or stimulus to thesubject. In one variation, the method further comprises communicatinginformation to the subject regarding the measured brain activity.

[0029] In another embodiment, software is provided for guiding brainactivity training, the software comprising: computer executable logicfor selecting a behavior or stimulus for activating one or more regionsof interest of a subject based, at least in part, on a measured brainactivity; and logic for communicating the selected behavior or stimulusto the subject. In one variation, the software further comprises logicthat communicates information to the subject regarding the measuredbrain activity.

[0030] In another embodiment, a computer assisted method is provided forguiding brain activity training comprising: having a subject perform afirst behavior or be exposed to a first stimulus; measuring activity ofone or more regions of interest of the subject in response to the firstbehavior or first stimulus; and employing computer executable logic toselect a second behavior or a second stimulus for activating the one ormore regions of interest based, at least in part, on the measured brainactivity; and having the subject perform the second behavior or beexposed to the second stimulus. Optionally, the method further comprisesemploying computer executable logic to communicate to the subject theselected second behavior or second stimulus. In another embodiment, acomputer assisted method is provided for guiding brain activity trainingcomprising: instructing a subject to perform a first behavior orcommunicating a first stimulus to the subject; measuring activity of oneor more regions of interest of the subject in response to the firstbehavior or first stimulus; and employing computer executable logic toselect a second behavior or a second stimulus for activating the one ormore regions of interest based, at least in part, on the measured brainactivity; and instructing the subject to perform the second behavior orcommunicating the second stimulus to the subject.

[0031] Computer executable software is provided for guiding brainactivity training, the software comprising: logic for communicatinginstructions to a subject to perform a first behavior and/or a firststimulus to the subject; logic for taking activity measurements of oneor more regions of interest of the subject in response to the firstbehavior or first stimulus and selecting a second behavior or a secondstimulus for activating the one or more regions of interest based, atleast in part, on the measured brain activity; and logic forcommunicating instructions to the subject to perform the second behaviorand/or the second stimulus to the subject.

[0032] In another embodiment, computer executable software is providedfor guiding brain activity training, the software comprising: logic formeasuring activity of one or more regions of interest of the subject inresponse to a first behavior or first stimulus; logic for selecting asecond behavior or a second stimulus for activating the one or moreregions of interest based, at least in part, on a measured brainactivity; logic for communicating to the subject the selected secondbehavior or second stimulus.

[0033] In another embodiment, a method is provided for directingtraining of one or more regions of interest of a subject, the methodcomprising: continuously measuring activity in the one or more regionsof interest of the subject; and employing computer executable logic todetermine when to communicate information to the subject based, at leastin part, on the measured activities. It is noted that the computerexecutable logic may optionally compute activity metrics from themeasured activity and base the selection on the activity metrics. Thecomputer executable logic may determine when to communicate informationbased on when the computed activity metric satisfies a predeterminedcondition, such as a target activity metric. It is noted that theinformation may be instructions, stimuli, physiological measurementrelated information, and/or subject performance related information. Inone variation, the instructions are instructions to perform a behavior.

[0034] In another embodiment, a method is provided for directingtraining of one or more regions of interest of a subject, the methodcomprising: measuring activity in the one or more regions of interest ofthe subject; determining one or more activity metrics for the measuredactivity; determining when the one or more activity metrics satisfy apredetermined condition, and communicating information to the subject;wherein these steps are repeatedly performed in substantially real time.

[0035] In another embodiment, software is provided for directingtraining of one or more regions of interest of a subject, the softwarecomprising: logic for taking measurements of activity of the one or moreregions of interest of the subject and determining one or more activitymetrics for the measured activity; logic for determining when the one ormore activity metrics satisfy a predetermined condition; and logic forcausing information to be communicated to the subject; wherein thesoftware is able to determine the activity metrics from the activitymeasurements and cause information to be communicated in substantiallyreal time.

[0036] In another embodiment, a method is provided for directingtraining, the method comprising: measuring activities of one or moreregions of interest; determining when the measured activities havereached a desired state; and communicating information to a subjectregarding when to perform a next behavior when the measured activitieshave reached the desired state.

[0037] In another embodiment, a method is provided for directingtraining, the method comprising: measuring activities of one or moreregions of interest; determining when the measured activities havereached a desired state; and communicating a stimulus to a subject whenthe measured activities have reached the desired state.

[0038] In another embodiment, computer executable software is provided,the software comprising: logic for taking activities of one or moreregions of interest and determining when the measured activities havereached a desired state; and logic for causing information to becommunicated to a subject regarding when to perform a next behavior whenthe measured activities have reached the desired state.

[0039] In another embodiment, computer executable software is provided,the software comprising: logic for taking measuring activities of one ormore regions of interest and determining when the measured activitieshave reached a desired state; and logic for causing a stimulus to becommunicated to a subject when the measured activities have reached thedesired state.

[0040] In another embodiment, a method is provided for directingtraining of one or more regions of interest of a subject, the methodcomprising: measuring activity in the one or more regions of interest ofthe subject; determining one or more activity metrics for the measuredactivity; determining when the one or more activity metrics satisfy apredetermined condition; and communicating a performance reward to thesubject; wherein these steps are repeatedly performed in substantiallyreal time. In one variation, the activity metrics measure a similaritybetween the spatial pattern of activity within the region of interestand a target spatial pattern of activity.

[0041] In another embodiment, software is provided for directingtraining of one or more regions of interest of a subject, the softwarecomprising: logic for taking measurements of activity of the one or moreregions of interest of the subject and determining one or more activitymetrics for the measured activity; logic for determining when the one ormore activity metrics satisfy a predetermined condition; and logic forcausing a performance reward to be communicated to the subject; whereinthe software is able to determine the activity metrics from the activitymeasurements and cause information to be communicated in substantiallyreal time.

[0042] In another embodiment, a method is provided for directingtraining of one or more regions of interest of a subject, the methodcomprising: measuring activity in the one or more regions of interest ofthe subject; determining what information is to be communicated to thesubject based, at least in part, on the measured activity; wherein thesesteps are repeatedly performed in substantially real time. In onevariation, the communicated information is a representation of themeasured activity. In another variation, the communicated information isan instruction to the subject.

[0043] In another embodiment, a method is provided for directingtraining of one or more regions of interest of a subject, the methodcomprising: measuring activity in the one or more regions of interest ofthe subject; determining one or more activity metrics for the measuredactivity; determining when the one or more activity metrics satisfy apredetermined condition; and selecting information to be communicated tothe subject based on the satisfaction of the predetermined condition. Ina preferred embodiment, these steps are continuously performed. In onevariation, the communicated information is a representation of themeasured activity. In another variation, the communicated information isan instruction to the subject.

[0044] In another embodiment, software is provided for directingtraining of one or more regions of interest of a subject, the softwarecomprising: logic taking measurements of activity of the one or moreregions of interest of the subject and determining what information isto be communicated to the subject based, at least in part, on themeasured activity; wherein the software is capable of taking themeasurements of activity and determining what information is to becommunicated in substantially real time. In one variation, thecommunicated information is a representation of the measured activity.In another variation, the communicated information is an instruction tothe subject.

[0045] In another embodiment, software is provided for directingtraining of one or more regions of interest of a subject, the softwarecomprising: logic taking measurements of activity of the one or moreregions of interest of the subject and determining one or more activitymetrics for the measured activity; logic for determining when the one ormore activity metrics satisfy a predetermined condition; and logic forselecting information to be communicated to the subject based on thesatisfaction of the predetermined condition. In a preferred embodiment,the software is capable of taking the measurements of activity andselecting the information to be communicated in substantially real time.

[0046] In another embodiment, a computer assisted method is provided forguiding brain activity training comprising: measuring activity of one ormore regions of interest of a subject; employing computer executablesoftware to determine information to communicate to the subject based,at least in part, on the measured brain activity; and employing computerexecutable software to communicate the information to the subject.

[0047] In another embodiment, a computer assisted method is provided forguiding brain activity training, the method comprising: measuringactivity of one or more regions of interest of a subject; employingcomputer executable software to determine instructions based, at leastin part, on the measured brain activity; and employing computerexecutable software to communicate the instructions to the subject. Inone variation, measuring activity comprises recording activity data froma scanner, converting the recorded activity data to image data, andpreprocessing the image data; and communicating the informationcomprises displaying images derived from the preprocessing image data.

[0048] In another embodiment, a method is provided for directingtraining of one or more regions of interest of a subject, the methodcomprising: measuring activity in the one or more regions of interest ofthe subject; determining how to communicate information to the subjectbased, at least in part, on the measured activity; wherein these stepsare repeatedly performed in substantially real time.

[0049] In another embodiment, software is provided for directingtraining of one or more regions of interest of a subject, the softwarecomprising: logic taking measurements of activity of the one or moreregions of interest of the subject and determining how information is tobe communicated to the subject based, at least in part, on the measuredactivity; wherein the software is capable of taking the measurements ofactivity and determining how information is to be communicated insubstantially real time.

[0050] In another embodiment, a method is provided for selectivelyactivating one or more regions of interest, the method comprising: (a)communicating one or more stimuli to a subject and/or having the subjectperform one or more behaviors that are directed toward activating theone or more regions of interest without measuring activation of the oneor more regions of interest; and (b) communicating the same one or morestimuli to the subject and/or having the subject perform the samebehaviors as in step (a) in combination with measuring brain activity inthe one or more regions of interest as the subject is exposed to stimuliand/or performs the behaviors. In one variation, information isdisplayed to the subject in step (a) that simulates the information thatis displayed to the subject during step (b).

[0051] In another embodiment, software is provided for use in training,the software comprising logic for communicating information to guide asubject in the performance of a training exercise during whichactivation is not measured; and logic for communicating information toguide a subject in the performance of a training exercise during whichactivation of one or more regions of interest is measured; whereininformation is displayed to the subject when activity is not measuredthat simulates activity measurements that are displayed when activity ismeasured. In another embodiment, a method is provided for selectivelyactivating one or more regions of interest, the method comprising:communicating information to a subject that instructs a subject toperform a sequence of behaviors or have a series of perceptions that areadapted to cause the selective activation of one or more regions ofinterest.

[0052] In another embodiment, a method is provided for selectivelyactivating one or more regions of interest, the method comprising:identifying information that instructs a subject to perform a sequenceof behaviors or have a series of perceptions that selectively causesactivation of one or more brain regions in a subject; communicating theidentified information to a same or different subject; and measuringactivation of one or more regions of interest in response to thecommunicated information.

[0053] In another embodiment, software is provided for use in training,the software comprising logic for communicating information to guide asubject in the performance of a training exercise during whichactivation of one or more regions of interest is not measured, the logicdisplaying information that simulates activity measurements of the oneor more regions of interest. In another embodiment, software andinformation is provided for use in training, the software comprisinglogic for communicating information to guide a subject in theperformance of a training exercise during which activation is notmeasured, and the information comprising stimuli, instructions, and/ormeasured information having been determined based in part upon activityin a region of interest during a training period when activity wasmeasured and communicated to the same or a different subject insubstantially real time.

[0054] In another embodiment, a method is provided for selecting how toachieve activation of one or more regions of interest, the methodcomprising: (a) having a subject perform a set of behaviors; (b)measuring how well each of the behaviors in the set activate the one ormore regions of interest; (c) selecting a subset of the behaviors fromthe set found to be effective in activating the one or more regions ofinterest; and (d) after step (c) and in the absence of measuringactivation, determining what information to communicate to the same or adifferent subject based, at least in part, on the activity measurementsof step (b). In one variation, evaluating the set of behaviors comprisescalculating and comparing activation metrics computed for each behaviorbased on measured activities for the different behaviors. In anothervariation, the behaviors evaluated are overt behaviors involving aphysical motion of the body of the subject. In another variation, thebehaviors are covert behaviors only cognitive processes which do notlead to a physical motion of the body of the subject. In the case whenthe subject in step (a) is different than the subject in step (d), thesubject in step (d) may have a commonality with the subject of step (a)in relation to the one or more regions of interest upon which thebehaviors were selected.

[0055] In another embodiment, computer executable logic is provided forselecting how to achieve activation during training of one or moreregions of interest of a subject, the software comprising: logic forcalculating activation metrics for activity measured for one or moreregions of interest in a first subject; logic for comparing a set ofcalculated activation metrics and selecting a subset of the activationmetrics having a superior activation of the one or more regions ofinterest in that first subject; logic that takes the measured brain fromthe first subject and determines for a second subject one or moremembers of the group consisting of: a) what next stimulus to communicateto the second subject, b) what next behavior to instruct the secondsubject to perform, c) when the second subject is to be exposed to anext stimulus, d) when the second subject is to perform a next behavior,e) one or more activity metrics computed from the measured activity inthe first subject, f) a spatial pattern computed from the measuredactivity in the first subject, g) a location of a region of interestcomputed from the measured activity of the first subject, h) performancetargets that the second subject is to achieve computed from the measuredactivity in the first subject, i) a performance measure the secondsubject's success computed from the measured activity in the firstsubject; and logic for communicating information based on thedeterminations to the second subject. In one variation, the informationcommunicated to the second subject is communicated during a process oftraining. In another variation, the information communicated to thesecond subject is a set of instructions and/or stimuli to be used by thesecond subject in performing training trials. In another variation, theinformation communicated to the second subject is a set of instructionsand/or stimuli to be used by the second subject in performing trainingtrials for the activation of a brain region of interest in the secondsubject.

[0056] In another embodiment, computer executable logic is provided forselecting how to achieve activation during training of one or moreregions of interest of a subject, the software comprising: logic forcalculating activation metrics for activity measured for one or moreregions of interest during each of several behaviors in a first subject;logic for comparing a set of calculated activation metrics correspondingto the set of behaviors and selecting a subset of the activation metricsand their corresponding behaviors having a superior activation of theone or more regions of interest in that first subject; logic that takesthe measured brain activity from the first subject and determinesinformation to communicate to a second subject; and logic forcommunicating the determined information to the second subject. In onevariation, the logic communicates the determined information to thefirst subject in substantially real time relative to when the activityis measured.

[0057] In another embodiment, a method is provided for selecting how toachieve activation during training of one or more regions of interest ofa subject, the method comprising: calculating activation metrics foractivity measured for one or more regions of interest during each ofseveral behaviors in a first subject; and comparing a set of calculatedactivation metrics corresponding to the set of behaviors and selecting afirst subset of the activation metrics and their corresponding behaviorshaving a superior activation of the one or more regions of interest inthat first subject; at a later time: (a) having a second subject performa behavior adapted to selectively activate one or more regions ofinterest in the first subject; and (b) optionally communicatinginformation to the second subject based on the measured brain activityin the first subject; wherein steps (a)-(b) are repeated multiple times,the second subject using the communicated information to guide thesecond subject in the subsequent performance of the behavior. In onevariation, computer executable logic is employed to select theinformation communicated to the subject. In another variation, computerexecutable logic is employed to cause the information to be communicatedto the second subject. In one variation, the first subject and thesecond subject are the same subject. In another variation, the firstsubject and the second subject are different subjects. In the case whenthe first and the second subject are different subjects, the secondsubject may additionally have been selected based upon having acondition likely to benefit from similar training as that received byfirst subject.

[0058] In another embodiment, a computer assisted method is provided forguiding brain activity training comprising: measuring activity of one ormore internal voxels of a brain; employing computer executable logicthat takes the measured brain activity and determines one or moremembers of the group consisting of: a) what next stimulus to communicateto the subject, b) what next behavior to instruct the subject toperform, c) when a subject is to be exposed to a next stimulus, d) whenthe subject is to perform a next behavior, e) one or more activitymetrics computed from the measured activity, f) a spatial patterncomputed from the measured activity, g) a location of a region ofinterest computed from the measured activity, h) performance targetsthat a subject is to achieve computed from the measured activity, i) aperformance measure of a subject's success computed from the measuredactivity, j) a subject's position relative to an activity measurementinstrument; and communicating information based on the determinations tothe subject in substantially real time relative to when the activity ismeasured.

[0059] Computer executable software for guiding brain activity trainingis also provided that comprises: logic which takes data corresponding toactivity measurements of one or more internal voxels of a brain anddetermines one or more members of the group consisting of: a) what nextstimulus to communicate to the subject, b) what next behavior toinstruct the subject to perform, c) when a subject is to be exposed to anext stimulus, d) when the subject is to perform a next behavior, e) oneor more activity metrics computed from the measured activity, f) aspatial pattern computed from the measured activity, g) a location of aregion of interest computed from the measured activity, h) performancetargets that a subject is to achieve computed from the measuredactivity, i) a performance measure of a subject's success computed fromthe measured activity, j) a subject's position relative to an activitymeasurement instrument; and logic for communicating information based onthe determinations to the subject in substantially real time relative towhen the activity is measured.

[0060] Computer executable software is also provided for guiding brainactivity training that comprises logic which takes a measurement ofbrain activity in one or more regions of interest of a subject while thesubject has one or more perceptions and/or performs one or morebehaviors that are directed toward activating the one or more regions ofinterest and determines one or more members of the group consisting ofa) what next stimulus to expose the subject to, b) what next behavior tohave the subject perform, c) what information to communicate to thesubject, d) when a subject is exposed to the next stimulus, e) when thesubject is to perform the next behavior, f) when new information is tobe communicated to the subject, g) how a subject is exposed to the nextstimulus, h) how the subject is to perform the next behavior, and i) hownew information is to be communicated to the subject. In one variation,the software performs the determinations in substantially real timerelative to when the brain activity measurement is taken. In anothervariation, the determined information is communicated to the subject.

[0061] In another embodiment, a method for guiding brain activitytraining is provided that comprises: having a subject perform a behavioror be exposed to a stimulus; measuring activity of the one or moreregions of interest as the behavior is performed or the subject isexposed to the stimulus; and communicating information to the subjectbased on the measured brain activity in substantially real time relativeto when the behavior is performed or the subject is exposed to thestimulus.

[0062] In another embodiment, computer executable software is providedfor guiding brain activity training, the software comprising: logic forinstructing a subject to perform a behavior; logic for taking activitymeasurements of one or more regions of interest as the behavior isperformed and communicating information to the subject based on themeasured brain activity in substantially real time relative to when thebehavior is performed.

[0063] In another embodiment, a method is provided for guiding brainactivity training, the method comprising: (a) having a subject perform abehavior adapted to selectively activate one or more regions ofinterest; (b) measuring activity of the one or more regions of interestas the behavior is performed; and (c) communicating information to thesubject based on the measured brain activity in substantially real timerelative to when the behavior is performed; wherein steps (a)-(c) arerepeated multiple times, the subject using the communicated informationto guide the subject in the subsequent performance of the behavior. Inone variation, computer executable logic is employed to select theinformation communicated to the subject. In another variation, computerexecutable logic is employed to cause the information to be communicatedto the subject.

[0064] In another embodiment, computer executable software is providedfor guiding brain activity training, the software comprising: logic fortaking activity measurements of one or more regions of interest as abehavior is performed; and logic for communicating information to thesubject based on the measured brain activity in substantially real timerelative to when the behavior is performed; wherein the logic takes newactivity measurements as they are received and communicates newinformation based on the new activity measurements. In one variation,the software is able to take the activity measurements and cause theinformation to be communicated in substantially real time. In anothervariation, the software further includes logic for selecting whatinformation is to be communicated.

[0065] In another embodiment, a method is provided for diagnosing acondition of a subject associated with particular activation in one ormore regions of interest, the method comprising: having the subjectperform a behavior or have a perception adapted to selectively activateone or more regions of interest associated with the condition; measuringactivity of the one or more regions of interest as the behavior isperformed or the subject has the perception; and diagnosing a conditionassociated with the one or more regions of interest based on theactivity in response to the behavior or perception.

[0066] In another embodiment, a computer assisted method is provided fordiagnosing a condition of a subject associated with particularactivation in one or more regions of interest, the method comprising:having computer executable logic cause instructions to perform abehavior and/or a stimulus be communicated to the subject, the behaviorand/or stimulus being adapted to selectively activate one or moreregions of interest associated with the condition; having computerexecutable logic take activity measurements of the one or more regionsof interest in response to the behavior and/or stimulus and diagnosewhether the condition is present based on the activity response to thebehavior and/or stimulus.

[0067] In another embodiment, a method is provided for designing atreatment for a condition of a subject, the method comprising:identifying a behavior or stimulus adapted to selectively activate oneor more regions of interest associated with a condition to be treated;having the subject perform the selected behavior or exposing the subjectto the selected stimulus; measuring activity of the one or more regionsof interest as the behavior is performed or the subject is exposed tothe stimulus in order to evaluate the effectiveness of the treatment. Inone variation, the method further comprises identifying the one or moreregions of interest of a subject associated with the condition to betreated.

[0068] In another embodiment, computer executable software is providedfor designing a treatment for a condition of a subject, the softwarecomprising: logic for identifying a behavior or stimulus adapted toselectively activate one or more regions of interest associated with acondition to be treated; logic for instructing the subject to performthe selected behavior and/or communicating the selected stimulus to thesubject; and logic for taking activity measurements of the one or moreregions of interest as the behavior is performed or the subject isexposed to the stimulus and evaluating the effectiveness of thetreatment. In one variation, the software further comprises logic foridentifying the one or more regions of interest of a subject associatedwith the condition to be treated.

[0069] In another embodiment, a method is provided for treating one ormore regions of interest of a brain of a subject, the method comprising:having a subject perform a behavior or have a perception adapted toactivate one or more regions of interest where the resulting activity ofthe one or more regions of interest is measured as the behavior isperformed or the subject is exposed to the stimulus. In one variation,information selected from the group consisting of instructions, stimuli,physiological measurement related information, and subject performancerelated information is communicated to the subject as the behavior isperformed or the perceptions are being made. In another variation,information selected from the group consisting of instructions, stimuli,physiological measurement related information, and subject performancerelated information is communicated to the subject as the behavior isperformed or the perceptions are being made, the informationcommunicated to the subject is selected based, at least in part, on themeasured activity. In one variation, the one or more regions of interestselected are implicated in the etiology of a condition that the subjecthas. In another variation, the one or more regions of interest selectedare related to a disease state. In another variation, the one or moreregions of interest selected have an abnormality related to a diseasestate. In another variation, the one or more regions of interest areadjacent to a region of the brain that has been injured.

[0070] In another variation, a method is provided for selecting a brainregion of interest, the method comprising: having a subject perform abehavior or have a perception adapted to activate one or more localizedregions of the brain; measuring activity of the localized regions of thebrain of the subject as the behavior is performed or the perception ismade; and identifying one or more localized regions of the brain of thesubject whose activation changes in response to the behavior orperception. In one variation, the method further comprises storing alocation of the identified one or more regions of interest to memory. Inone variation, identifying the one or more localized regions of thebrain is performed less than 10, 5, 1, 0.1 minutes after the behavior isperformed or the perception is had.

[0071] In another variation, computer executable software is providedfor selecting a brain region of interest, the software comprising: logicfor instructing a subject perform a behavior adapted to activate one ormore localized regions of the brain; logic for taking activitymeasurements of the regions of interest of the subject as the behavioris performed and identifying one or more regions of interest of thesubject whose activation changes in response to the behavior orperception. In one variation, the software further comprises logic forselecting coordinates corresponding to the identified one or moreregions of interest. In another variation, the software furthercomprises logic for selecting coordinates corresponding to theidentified one or more regions of interest and storing the selectedcoordinates to memory.

[0072] In another embodiment, a method is provided for selecting a brainregion of interest, the method comprising: having a subject perform abehavior or have a perception; measuring activity of the regions ofinterest of the subject as the behavior is performed or the perceptionis made; and identifying one or more regions of interest of the subjectwhose activation changes in response to the behavior or perception.

[0073] In another embodiment, a computer assisted method is provided forevaluating an effectiveness of brain activity training comprising:selecting a target level of activation for one or more regions ofinterest of a subject; having the subject perform a behavior or have aperception; measuring activity of one or more regions of interest of asubject; employing computer executable software to compare the measuredactivity to the target level of activity. In one variation, the targetlevel of activity is communicated to the subject. In another variation,the target level of activity is displayed to the subject as the subjectperforms the behavior or has the perception. In yet another variation,the comparison between the measured activity and the target level ofactivity is communicated to the subject. In yet another variation, thecomparison between the measured activity and the target level ofactivity is displayed to the subject. In yet another variation, thecomputer executable software selects information to be communicated tothe subject based on the comparison between the measured and targetlevels of activity. In yet another variation, the software selectsinstructions to be communicated to the subject based on the comparisonbetween the measured and target levels of activity. In yet anothervariation, the software selects a behavior to be performed or a stimulusto expose the subject to based on the comparison between the measuredand target levels of activity. In yet another variation, comparingcomprises computing one or more members of the group consisting of avector difference, a vector distance, and a dot product between twovectorized spatial patterns of physiological activity.

[0074] In another embodiment, computer executable software is providedfor evaluating an effectiveness of brain activity training, the softwarecomprising: logic for selecting a target level of activation for one ormore regions of interest of a subject; logic for communicatinginstructions to the subject to perform a behavior and/or communicate astimulus to the subject; logic for taking activity measurements of oneor more regions of interest of a subject and comparing the measuredactivity to the target level of activity. In one variation, the softwarecomprises logic for communicating the target level of activity to thesubject. In another variation, the software comprises logic for causingthe target level of activity to be displayed to the subject as thesubject performs the behavior or as the stimulus is communicated. In yetanother variation, the software comprises logic that communicates thecomparison between the measured activity and the target level ofactivity to the subject. In yet another variation, the softwarecomprises logic for displaying the comparison between the measuredactivity and the target level of activity to the subject. In yet anothervariation, the software comprises logic for selecting information to becommunicated to the subject based on the comparison between the measuredand target levels of activity. In yet another variation, the softwarecomprises logic for selecting instructions to be communicated to thesubject based on the comparison between the measured and target levelsof activity. In yet another variation, the software comprises logic forselecting a behavior to be performed or a stimulus to communicate to thesubject based on the comparison between the measured and target levelsof activity. In yet another variation, the logic for comparing compriseslogic for computing one or more members of the group consisting of avector difference, a vector distance, and a dot product between twovectorized spatial patterns of physiological activity.

[0075] In another embodiment, a training method is provided thatcomprises: having a subject perform a behavior or be exposed to astimulus; measuring activity of the one or more regions of interest asthe behavior is performed or the subject is exposed to the stimulus; andhaving the subject estimate the measured activity. In one variation, nobehavior or stimulus may be used. In another variation, the behaviorused is the cognitive process of forming an estimate of measuredactivity. In one variation, the method further comprises communicatinginformation to the subject regarding how well the subject estimated themeasured activity. In another variation, the subject inputs his or herestimate into a system. In another variation, the method furthercomprises recording to memory how well the subject estimated themeasured activity. In another variation, an activity metric iscalculated based on the measured activity and the subject estimates theactivity metric. It is noted that the subject's estimate of the measuredactivity can be a qualitative estimate (e.g., higher than a value, lowerthan a value) or quantitative (e.g., a numerical estimate).

[0076] In another embodiment, computer executable software is providedthat comprises: logic for taking activity measurements for one or moreregions of interest; and logic for receiving a subject's estimate ofactivation of one or more regions of interest in response to a behavioror perception and comparing that estimate to the measured activation forone or more regions of interest. In one variation, the software furthercomprises logic for creating a displayable image illustrating thecomparison of the subject's estimate In another variation, the softwarefurther comprises logic for communicating information to the subjectregarding how well the subject estimated the measured activation. Inanother variation, the logic stores the estimate and activationmeasurements to memory. In another variation, the logic calculates anactivity metric based on the measured activation. In another variation,the subject's estimate is an estimated activity metric and the logiccompares an activity metric based on the measured activation to thesubject's estimated activity metric. It is noted that the subject'sestimate of the measured activity can be a qualitative estimate (e.g.,higher than a value, lower than a value) or quantitative (e.g., anumerical estimate).

[0077] Also according to any of the above embodiments, the behavior mayoptionally be selected from the group consisting of sensory perceptions,detection or discrimination, motor activities, cognitive processes,emotional tasks, and verbal tasks.

[0078] Also according to any of the above embodiments, the methods areoptionally performed with the measurement apparatus remaining about thesubject during the method. According to any of the above embodiments, inone variation, measuring activation is performed by fMRI.

[0079] According to any of the above embodiments, in one variation, theactivity measurements are made using an apparatus capable of takingmeasurements from one or more internal voxels without substantialcontamination of the measurements by activity from regions interveningbetween the internal voxels being measured and where the measurementapparatus collects the data.

[0080] Also according to any of the above embodiments, pretraining isoptionally performed as part of the method.

[0081] Also according to any of the above embodiments, in one variation,at least one of the regions of interest is an internal region of thebrain.

[0082] Also according to any of the above embodiments, in one variation,the one or more localized regions are all internal relative to a surfaceof the brain.

[0083] Also according to any of the above embodiments, in one variation,the one or more regions of interest comprise a voxel.

[0084] Also according to any of the above embodiments, in one variation,the one or more regions of interest comprise a plurality of differentvoxels.

[0085] According to any of the above embodiments, in one variation, theone or voxels measured has a two dimensional area. The two dimensionalarea optionally has a diameter of 50, 30, 20, 15, 10, 5, 4, 3, 2, 1,0.5, 0.1 mm or less.

[0086] According to any of the above embodiments, in one variation, theone or more voxels measured has a three dimensional volume. The threedimensional volume optionally has a volume of 22×22×12 cm, 11×11×6 cm,6×6×6 cm, 3×3×3 cm, 1×1×1 cm, 0.5×0.5×0.5 cm, 1×1×1 mm, 100×100×100microns or less.

[0087] Also according to any of the above embodiments, in one variation,measurements are made from at least 100 separate internal voxels, andthese measurements are made at a rate of at least once every fiveseconds.

[0088] Also according to any of the above embodiments, in one variation,measurements are made from a set of separate internal voxelscorresponding to a scan volume including the entire brain.

[0089] According to any of the above embodiments, the one or moreregions of interest optionally include one or members of the groupconsisting of neuromodulatory centers or plasticity centers.

[0090] Also according to any of the above embodiments, the methods maybe performed in combination with the administration of an agent forenhancing measurement sensitivity of the one or more regions ofinterest. For example, in one variation, the method is performed incombination with the administration of a fMRI contrast agent. In anothervariation, the method is performed in combination with theadministration of an agent that enhances activity in the one or moreregions of interest.

[0091] According to any of the above embodiments, measuring brainactivity is optionally performed continuously as the subject performs abehavior, has a perception and/or is exposed to a stimulus. For example,measuring brain activity is optionally performed at least every 10, 5,4, 3, 2, or 1, 0.1, 0.01 seconds or less as the subject performs abehavior, has a perception and/or is exposed to a stimulus.

[0092] According to any of the above embodiments, the subjects performsone or more behaviors during measurement that constitute training toactivate one or more brain region of interest.

[0093] According to any of the above embodiments, the method is used toguide brain activity training by instructing a subject to modulate abrain region of interest.

[0094] According to any of the above embodiments, an action is performedin response to a brain activity measurement in substantially real time.For example, an action is optionally performed in response to a brainactivity measurement at least every 10, 5, 4, 3, 2, or 1, 0.1, 0.01seconds or less.

[0095] Also according to any of the above embodiments, the behavior isoptionally a cognitive task the subject is to perform based on an imagedisplayed to the subject.

[0096] Also according to any of the above embodiments, in one variation,communicating information to the subject (for example: instructions,stimuli, physiological measurement related information, and subjectperformance related information) is performed by one or more of themembers selected from the group consisting of providing audio to thesubject, providing a smell to the subject, displaying an image to thesubject.

[0097] Also according to any of the above embodiments, a desiredactivity metric to be achieved optionally is determined and/orcommunicated.

[0098] Also according to any of the above embodiments, whether a desiredactivity metric is achieved optionally is determined and/orcommunicated.

[0099] Also according to any of the above embodiments, an activitymetric is optionally determined and/or communicated from measuredactivity. In one variation, the activity metric is modified relative toa baseline level of activation. In another variation, the activitymetric is normalized relative to a baseline level of activation. Inanother variation, a comparison between an activity metric and areference activity metric is performed.

[0100] Also according to any of the above embodiments, a measuredactivity metric may optionally be determined and/or communicated. In onevariation, the activity metric is modified relative to a baseline levelof activation. In another variation, the activity metric is normalizedrelative to a baseline level of activation. In another variation, acomparison between an activity metric and a reference activity metric isperformed.

[0101] Also according to any of the above embodiments, a measuredactivation image or volume may optionally be determined and/orcommunicated. In one variation, the activation image or volume ismodified relative to a baseline level of activation. In anothervariation, the activation image or volume is normalized relative to abaseline level of activation. In another variation, a comparison betweenan activation image or volume and a reference activation image or volumeis performed.

[0102] Also according to any of the above embodiments, in one variation,the subject performs a behavior, has a perception and/or is exposed to astimulus repeatedly for a period of at least 1, 5, 10, 20, 30, 60 ormore minutes.

[0103] Also according to any of the above embodiments, in one variation,the subject performs a behavior, has a perception and/or is exposed to astimulus repeatedly at least 2, 3, 4, 5, 10, 20, 100 or more minutes.

[0104] Also according to any of the above embodiments, in one variation,activity measurements are recorded to memory during the method.Optionally, activity measurements and the behaviors and/or stimuli usedare recorded to memory during the method. Optionally, any informationcommunicated to the subject is also recorded to memory.

[0105] Also according to any of the above embodiments, in one variation,activity measurements may be communicated to a remote location.Optionally, activity measurements and the behaviors and/or stimuli usedcommunicated to a remote location during the method. Optionally, anyinformation communicated to the subject is also communicated to a remotelocation. In one example, this communication to a remote location takesplace via internet communication. In another example, this communicationto a remote location takes place via wireless communication.

[0106] According to any of the above embodiments where information iscommunicated, in one variation, the information is communicated by amanner selected from the group consisting of providing audio to thesubject, providing tactile stimuli to the subject, providing a smell tothe subject, displaying an image to the subject.

[0107] According to any of the above embodiments wherein information isdetermined, in one variation, the information is determined while theinstrument used for measurement remains positioned about the subjectAlso according to any of the above embodiments wherein information iscommunicated, in one variation, the information communicated is aninstruction to the subject.

[0108] Also according to any of the above embodiments whereininformation is communicated, in one variation, the instruction is a textor iconic indication denoting an action that a subject is to perform.

[0109] Also according to any of the above embodiments whereininformation is communicated, in one variation, the instructionidentifies a task to be performed by the subject.

[0110] Also according to any of the above embodiments whereininformation is communicated, in one variation, some of the informationcommunicated to the subject is material to be learned.

[0111] Also according to any of the above embodiments wherein aninstruction is determined, in one variation, the instruction isdetermined by computer executable logic.

[0112] Also according to any of the above embodiments wherein aninstruction is communicated, in one variation, the instructioncommunicated is selected from a set of instructions stored in memory,the selection being based upon the brain activity measured.

[0113] Also according to any of the above embodiments, the subject mayoptionally input information to the system while brain activitymeasurements are being taken or while the subject is in a position wherebrain activity measurements may be taken.

[0114] Also according to any of the above embodiments, in one variation,the method further comprises selecting one or more of the internalvoxels to correspond to a region of interest for a particular subjectand using the selected internal voxels of the region of interest to makethe one or more determinations.

[0115] Also according to any of the above embodiments, in one variation,the region of interest is selected from the group consisting of one ofthe regions listed in FIG. 14, including the substantia nigra,subthalamic nucleus, nucleus accumbens, locus coeruleus, periaqueductalgray matter, nucleus raphe dorsalis, nucleus basalis of Meynert,dorsolateral pre-frontal cortex.

[0116] Also according to any of the above embodiments, in one variation,the region of interest has a primary function of releasing aneuromodulatory substance, where the neuromodulatory substance isselected from the group consisting of: dopamine, acetyl choline,noradrenaline, serotonin, an endogenous opiate.

[0117] Also according to any of the above embodiments, in one variation,the subject has one or more of the following conditions: Parkinson'sdisease, Alzheimer's disease, attention & attention deficit disorder,depression, substance abuse & addiction, schizophrenia.

[0118] These and other embodiments and variations of the methods,software and systems of the present invention are described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0119]FIG. 1 is an overview diagram of methods, components and processesof this invention.

[0120]FIG. 2 is a table of brain regions.

[0121]FIG. 3 is a table of neurological, psychological and otherconditions.

[0122]FIG. 4 is a diagram of methods and apparatus for displayinginformation to a subject in a measurement apparatus.

[0123]FIG. 5 is a table of functional MRI scanning parameters.

[0124]FIG. 6 is an example display screen that may be presented.

[0125]FIG. 7 is an example of a display screen that may be used forlocalizing a region of interest.

[0126]FIG. 8 shows examples of display panels that may be presented.

[0127]FIG. 9 shows further examples of display panels that may bepresented.

[0128]FIG. 10 shows an example time progression of displays on an ROIpanel, and the structure of an example trial.

[0129]FIG. 11 shows examples of display panels that may be presented.

[0130]FIG. 12 shows further examples display panels that may bepresented.

[0131]FIG. 13 shows a diagram of an apparatus for stabilizing the headof a subject, which may be particularly suited for use in early andexperimental implementations of the device when free head-movementtechnology is not available.

[0132]FIG. 14 shows a table of brain regions that may be used as regionsof interest

DEFINITIONS

[0133] Activity, as used herein, refers to physiological activityassociated with one or more voxels of the brain whose physiologicalactivity may be monitored. Examples of types of physiological activityinclude, but are not limited to, neuronal activity, blood flow, bloodoxygenation, electrical activity, chemical activity, tissue perfusion,the level of a nutrient or trophic factor, the production ordistribution of a trophic factor, the production, release, or reuptakeof a neurotransmitter or neuromodulator, the growth of tissue such asneurons or parts of neurons, neural plasticity, and other physiologicalprocesses. Other examples are provided herein.

[0134] Activation, as used herein, refers to a change in activity in oneor more voxels of the brain whose physiological activity may bemonitored. This change may include an increase or decrease. It is notedthat this change may also include a change where some voxels increase inactivation at the same time that other voxels decrease in activation.

[0135] Activity metric, as used herein, refers to any computed measureof activity of one or more regions of interest of the brain.

[0136] Altering activity, as used herein, refers to an alteration inactivity levels in one or more regions of interest of the brain. It isnoted that altering activity can be an increase and/or a decrease inactivation. When a plurality of voxels of the brain are involved, all oronly some may have increased activity and all or only some may havedecreased activity. It should be recognized that some voxels may haveincreased activity while other voxels have decreased activity.

[0137] Anti-nociciptive regions, as used herein, refers to areas of thebrain which, when active, may produce a decrease or modulation in thesensation of experienced severity of pain.

[0138] Behavioral training, as used herein, refers to training a subjectto generate an overt action in response to a form of information that iscommunicated to the subject. It is noted that behavioral training maytake place in combination with training a subject to alter activity inone or more regions of interest.

[0139] Behavior, as used herein, refers to a physical or mental task orexercise engaged in by a subject, which may be in order to activate oneor more regions of interest of the brain. Examples of different types ofbehaviors include, but are not limited to sensory perception, detectionor discrimination, motor activities, cognitive processes such as mentalimagery or mental manipulation of an imagined object, reading, emotionaltasks such as attempting to create a particular affect or mood, verbaltasks such as listening to, comprehending, or producing speech. Otherexamples of behaviors are provided herein.

[0140] BOLD, as used herein refers to Blood Oxygen Level Dependentsignal. This signal is typically measured using a functional magneticresonance imaging device.

[0141] Condition, as used herein, refers to any physiological,psychological or health condition that may be treated according to thepresent invention by changing a level of activity in one or more regionsof interest associated with that condition. Numerous examples ofconditions that may be treated according to the present invention areprovided herein. It is noted that a condition may additionally refer toa normal state of a subject that one may desire to alter, such as thecondition of a subject s mood.

[0142] Device operator, as used herein, refers to an individual whocontrols the functioning of apparatus or software associated with thisinvention. It is to be noted that the device operator may be a personother than the subject, may be the subject, or may be a remotely locatedparty using appropriate communication technology such as an internetconnection.

[0143] Endopharmacology or endomedication, as used herein, refers to theactivation or modulation of a brain region that releases endogenousneuromodulatory substances or neurotransmitters onto one or more targetregions, and thereby regulates neuronal function.

[0144] Event related, as used herein, refers to an event that is relatedto a physiological activity which is caused by a known event, or takesplace immediately preceding or subsequent to that event. In a typicalexample, a stimulus or behavior event is repeated many times, and theaverage event related activity is the average activity level at a set ofdefined times relative to the onset time of the event. This may becomputed using a PETH.

[0145] Exemplar, as used herein, refers to an instance that serves as amember of a set. Exemplar stimuli are stimuli taken as instances from aset, such as a set of stimuli, the perception of which are thought toengage a particular region of interest. Exemplar behaviors are behaviorstaken as instances from a set, such as a set of behaviors, theperformance of which are thought to engage a particular region ofinterest.

[0146] Exercise, as used herein, refers to repeated training, such astraining designed to activate a brain region.

[0147] Existing MRI/fMRI/PET data processing packages, as used hereinrefers to the following packages, their documentation, websites, andcited literature references contained in their documentation andwebsites: SPM99 (and the SPM99 manual written by Dick Veltman and ChloeHutton, May 2001), Brain Voyager from Brian Innovation, AIR by RogerWoods, MRICro by Chris Rorden, AFNI by RW Cox, and other packages thatmay be developed to perform related functions.

[0148] Information, as used herein, refers to anything communicated tothe subject, whether by sight, sound, smell, contact with the subject,etc., relating to the performance of the various methods of the presentinvention. Examples of various types of information that may becommunicated to the subject include, but are not limited to,instructions, physiological measurement related information, subjectperformance related information, and stimulus information that causesthe subject to have a perception. Examples of ways of communicatinginformation include, but are not limited to displaying information tothe subject, playing audio for the subject, providing an agent for thesubject to smell, applying a physical force to the subject (e.g., apressure or vibration or proprioceptive stimulus), and causing aphysical sensation for the subject (e.g., cold, hot, pain, electricalcharge, etc.). Specific examples of information include, but are notlimited to images of the subject's brain activity pattern, charts of thetimecourse of physiological activity in a region of interest, or anactivity metric from a region of interest, instructions to perform atask or how to perform a task, movies, or stereoscopic virtual realitystimuli viewed through stereo viewers and designed to simulate certaincircumstances or experiences. Further examples include games played bythe subject, such as computer games.

[0149] Instructions, as used herein, refers to any instruction toperform a physical or mental action that is communicated to a subject oran operator assisting a subject. Examples of instructions include, butare not limited to instructions to a subject to perform a behavior;instructions to a subject to rest; instructions to a subject to move;instructions to a subject to make a computer input; instructions to asubject to activate a brain region, such as to a designated level.Further examples of instructions are provided herein.

[0150] Localized region, as used herein refers to any region of thebrain with a defined spatial extent. In one variation, a localizedregion measured by this invention may be internal relative to a surfaceof the brain.

[0151] Measurement information, as used herein, refers to anyinformation that communicates a measurement to a subject. Examples oftypes of measurements include, but are not limited to anatomicalmeasurements, physiological measurements, activity measurements,activity metrics computed from activity measurements, and activationimages.

[0152] Measurement of activity, as used herein, refers to the detectionof activity in one or more voxels of the brain. Once measured, activitymetrics may be computed from these measurements. Activity measurementsmay be performed by any measurement technology that is capable ofmeasuring activity in one or more voxels of the brain, or bycombinations of such technologies with other forms of measurement.Various suitable measurement technologies are described herein.

[0153] Neuromoanatomical texts, as used herein refers to any of avariety of texts describing the structures of the brain, including butnot limited to Fundamental Neuroanatomy by Nauta and Feirtag, and in theCo-Planar Steriotaxic Atlas of the Human Brain by Jean Talairach andPierre Tournoux, Magnetic Resonance Imaging of the Brain and Spine (2Volume Set) by Scott W., Md. Atlas.

[0154] Neuromodulator or neuromodulatory substance, as used herein,refers to compounds which can alter activity or responsiveness in one ormore localized regions of the brain. Examples of neuromodulatorsinclude, but are not limited to: opioids, neuropeptides, acetylcholine,dopamine, norepinephrine, serotonin and other biologic amines, andothers. Many pharmacologic agents such as morphine, caffeine and prozacare exogenous mimics of these neuromodulatory substances.

[0155] Neuromodulatory centers, as used herein, refers to regions of thebrain or nervous system that serve to regulate or alter responsivenessin other parts of the nervous system. Examples include regions thatcontain neurons that release neuromodulatory transmitters such ascatecholamines, acetylcholine, other biologic amines, neuropeptides,serotonin, norepinephrine, dopamine, adrenaline. These centers and theactions produced through their modulation are described in neuroanatomytexts and The Biochemical Basis of Neuropharmacology, Cooper, Bloom andRoth. Examples include but are not limited to the nucleus raphe magnus,substantia nigra (pars compacta and reticulata), nucleus accumbens,periaqueductal gray, locus coeruleus, nucleus basalis, red nucleus,nucleus accumbens.

[0156] PETH, as used herein, refers to a peri-event time histogram. Thisis a measure of the average value of an activity pattern metric basedupon multiple trials, for each of a set of fixed time intervals after aconditioning event such as a stimulus or the onset of a behavior.

[0157] Perception, as used herein, refers to a cognitive response by asubject that may result in the activation of one or more localizedregions of the brain. In some instances, the perception is in responseto stimulus information that is communicated to the subject. However,the perception may also be independent of stimulus being communicated tothe subject.

[0158] Performance target, as used herein, refers to an activity metricthat a subject may be instructed to achieve. The performance target maybe communicated to the subject in some manner before, during or after atrial.

[0159] Pharmacological treatment, as used herein, refers to theadministration of any type of drug, remedy, or medication.

[0160] Region of interest or ROI or volume of interest, as used herein,refers to a particular one or more voxels of the brain of a subject. AnROI may occasionally be referred to as an area or volume of interestsince the region of interest may be two dimensional (area) or threedimensional (volume). Frequently, it is an object of the methods of thepresent invention to monitor, control and/or alter brain activity in theregion of interest. For example, the one or regions of interest of thebrain associated with a given condition may be identified as the regionof interest for that condition. In one variation, the regions ofinterest targeted by this invention are internal relative to a surfaceof the brain.

[0161] Regulation or modulation, as used herein, refers to a subjectperforming a behavior or having a perception that controls activity in aregion of interest. Regulation may cause the activity to increase ordecrease relative to a desired level, or to change spatial pattern.Regulation may be monitored using one or more activity metric, forexample by monitoring for an increase, decrease, or maintenance in theactivity metric. Preferably, regulation provides control over activityfor at least a selected period of time (e.g. seconds, minutes, days, orlonger).

[0162] Reward centers or pleasure centers, as used herein, refers toareas of the brain which, when active, produce pleasurable or rewardingexperiences or sensations. These include, but are not limited to certainlimbic structures, the nucleus accumbens, locus coeruleus, septalnuclei, and others. These may also include areas that have beenassociated with addictive behaviors.

[0163] Reward, as used herein, refers to information, incentives, orobjects given or promised to subjects to encourage their positiveperformance in a task. These include numerical values of performancelevel such as percent correct, encouragement, enjoyable activities, ormonetary or other enticements toward correct performance.

[0164] Scan volume, as used herein, refers to a three dimensional volumewithin which brain activity is measured. This volume may be divided intoan array of voxels. For example, in the case of fMRI, a scanning volumemay correspond to a 3-D cube (e.g., 22×22×12 cm) that comprises thevolume of the head of a subject. This volume may be divided into a64×64×17 array of subvolumes (voxels).

[0165] Single point, as used herein, refers to an individual geometriclocus or small area of volume, such as a single small geometric volumefrom which a physiological measurement will be made, with the volumebeing 0.1, 0.5, 1, 2, 3, 4, 5, 10, 15, 20, 30, 50, 100 mm in diameter. Adevice making a measurement from a single point is contrasted with adevice making scanned measurements from an entire volume comprised ofmany single points.

[0166] Spatial array, as used herein, refers to a contiguous ornon-contiguous set of location points, areas or volumes in space. Thespatial array may be two dimensional in which case elements of the arrayare areas or three dimensional in which case elements of the array arevolumes.

[0167] Spatial pattern, or spatial activity pattern, or vectorizedspatial pattern, as used herein, refers to the measured activities ofthe set of voxels forming a two dimensional or three dimensional spatialarray such as a scan volume or portion of a scan volume. A vectorcomprising a rational or real value for each voxel in a threedimensional spatial array is one example of a spatial pattern. Sinceactivity associated with each voxel is represented, a spatial patterncontains much more information than a single activity metric for theentire localized region. It is noted that a spatial pattern may bedefined either in geometric space as physically measured, or may bedefined in a transformed space or standard coordinate space intended toallow the geometric points in the brain of one subject to be alignedwith anatomically or physiologically corresponding points in anothersubject or group of subjects.

[0168] Stimulus information, as used herein, refers to any informationwhich when communicated to a subject may cause the subject to have aperception, and/or to alter activity in one or more regions of interestof the subject's brain. Examples of stimulus information include but arenot limited to: displays of static or moving images, sounds, and tactilesensations. It should be recognized that certain types of informationmay perform a dual function of being stimulus information and alsocommunicating another type of information.

[0169] Stimulus set or behavior set, as used herein, refers to a definedset of stimuli or behaviors that are to be used to activate one or moreparticular regions of interest of a subject's brain. The exemplarsforming the set may constitute either a set of discrete exemplars (suchas a set of digitized photographic images of faces, instructions, orwords), or a continuum from which particular exemplars can be drawn(such as the sound frequencies from 2000-8000 Hz or visual gratings withspatial frequency from 0.01-10 cycles/degree of arc). As will bedescribed herein, a set of exemplars may be used to identify a subsetthat are found to more effectively activate the particular one or moreparticular regions of interest.

[0170] Subject, as used herein, refers to a person whose brain activityis to be measured in conjunction with performing the methods of thepresent invention. It is noted that the subject is the person who hasthe condition being treated by the methods of the present invention.

[0171] Subject performance related information, as used herein, refersto any information relating to how effectively a subject is alteringactivity in one or more regions of interest of the subject's brain beingtargeted, for example, in response to the subject performing a behavioror having a perception that is directed toward altering activity in oneor more particular regions of interest.

[0172] Substantially real time, as used herein, refers to a short periodof time between process steps. Preferably, something occurs insubstantially real time if it occurs within a time period of less than10 seconds, more preferably less than 5, 4, 2, 1, 0.5, 0.2, 0.1, 0.01seconds or less. In one particular embodiment, computing an activitymetric is performed in substantially real time relative to when thebrain activity measurement used to compute the activity metric wastaken. In another particular embodiment, communicating information basedon measured activity is performed in substantially real time relative towhen the brain activity measurement was taken. Because activity metricsand information communication may be performed in substantially realtime relative to when brain activity measurements are taken, it is thuspossible for these actions to be taken while the subject is still inposition to have his or her brain activity measured.

[0173] Task, as used herein, refers to a perceptual, cognitive,behavioral, emotional, or other activity undertaken by a subject,typically repetitively as part of a trial.

[0174] Treatment, as used herein, refers to the application of thisinvention to a subject with the intent of improving a condition of thesubject.

[0175] Trial, as used herein, refers to a period of time that mayinclude one or more rest periods and one or more instances or attemptsto perceive a stimulus or perform a behavior. Trials may be typicallyrepeated in blocks, and blocks may be repeated in sessions.

[0176] Training, as used herein, refers to the process of a subjectperceiving a stimulus or performing a behavior in combination withhaving activity be measured of a region of interest to be activated bythe perception or behavior.

[0177] Vectorized brain states, as used herein, refers to a measuredstate of the brain where the activity in each voxel of the brain may beseparately measured, as in a spatial activity pattern.

[0178] Voxel, as used herein, refers to a point or three dimensionalvolume from which one or more measurements are made. A voxel may be asingle measurement point, or may be part of a larger three dimensionalgrid array that covers a volume.

DETAILED DESCRIPTION OF THE INVENTION

[0179] The brain is the seat of psychological, cognitive, emotional,sensory and motoric activities. By its control, each of these elementsmay be controlled as well. Many psychological and neurologicalconditions arise because of inadequate levels of activity or inadequatecontrol over discretely localized regions within the brain. Theregulatory or neuromodulatory brain regions provide control over otherbrain regions. These regulatory or neuromodulatory brain regions causemany disease states when they fail to produce their intended regulation,and exogenous drugs often seek to re-apply this missing internalregulation.

[0180] The present invention provides methods, software, and systemsthat may be used to provide and enhance the activation and control ofone or more regions of interest, particularly through training andexercising those regions of interst. An overview diagram depicting thecomponents and process of the invention is presented in FIG. 1. Asillustrated, a scanner and associated control software 100 initiatesscanning pulse sequences, makes resulting measurements, and communicateselectronic signals associated with data collection software 110 thatproduces raw scan data from the electronic signals. The raw scan data isthen converted to image data corresponding to images and volumes of thebrain by the 3-D image/volume reconstruction software 120. The resultantimages or volume 125 is passed to the data analysis/behavioral controlsoftware 130. The data analysis/behavioral control software performscomputations on the image data to produce activity metrics that aremeasures of physiological activity in brain regions of interest. Thesecomputations include pre-processing 135, computation of activationimage/volumes 137, computation of activity metrics from brain regions ofinterest 140, and selection, generation, and triggering of informationsuch as measurement information, stimuli or instructions based uponactivity metrics 150, as well as the control of training and data 152,using the activity metrics and instructions or stimuli 160 as inputs.The results and other information and ongoing collected data may bestored to data files of progress and a record of the stimuli used 155.The selected instruction, measured information, or stimulus 170, is thenpresented via a display means 180 to a subject 190. This encourages thesubject to engage in imagined or performed behaviors or exercises 195 orto perceive stimuli. If the subject undertakes overt behaviors, such asresponding to questions, the responses and other behavioral measurements197 are fed to the data analysis/behavioral control software 130.

[0181] Through the use of the present invention, a subject is able to betrained to control the activation of a region of interest of thatsubject's brain, and then exercise the use of that region to furtherincrease the strength and control of its activation. This training andexercise can have beneficial effects for the subject. In the case ofregions that release endogenous neuromodulatory agents, this control canserve a role similar to that of externally applied drugs.

[0182] The exercise of regions of interest according to the presentinvention is analogous to the exercise provided by specialized trainingequipment for weight lifting that isolates the activation of aparticular set of muscles in order to build strength and control inthose muscles.

[0183] In addition to training and exercise, knowledge of the activationpattern in discrete brain regions can be used to enhance certain aspectsof a subject's behavioral performance, such as the subject's abilitiesat perception, learning and memory, and motoric skills. This enhancementtakes place by cuing a subject to perform a behavior at a point when ameasured pattern of brain activation is in a state correlated withenhanced performance. Alternatively, the behavior that the subject willundertake or the stimulus that the subject will perceive can be selectedbased upon the measured pattern of neural activation.

[0184] Methods have been described previously in the literature thatcorrespond to measuring a physiological property, and presenting themeasured result to the subject so that the subject can engage inbiofeedback. The present invention differs substantially from thosemethods. As described above, biofeedback has been employed inconjunction with certain brain recording methodologies, namely EEG (U.S.Pat. Nos. 4,919,143, 4,919,143, 5,406,957, 5,899,867 and 6,097,981) andlight (U.S. Pat. No. 5,995,857) to try to treat select brain disordersby allowing a subject to monitor his or her own brain functions (e.g.,blood flow or blood oxygenation or tissue metabolism) as the subjectattempts to alter a level of globalized brain function in response.These methods have typically been directed to monitoring of overallbrain activity of the entire brain or large areas of the brain usingsignals such as EEG brainwaves, and thereby allowing the subject to viewtheir own globalized activity level to try to learn relaxation, betterattention, or control over another global process.

[0185] The present invention is substantially different from the priorart, focusing upon using the discretely localized measurements emanatingfrom brain regions with very specific functions to control the stimuliand instructions presented to a subject. This control can be used intraining and exercise methods directed specifically to the functionscontrolled by the regions of interest being measured.

[0186] As will be explained herein, any brain measurement methodologymay be used in conjunction with the present invention so long as thephysiological activity of one or more discretely localized regions ofthe brain can be effectively monitored in substantially real time.

[0187] In one particularly important embodiment that will be describedin greater detail, the brain scanning methodology used is functionalmagnetic resonance imaging (fMRI).

[0188] In one variation, the regions of interest targeted by thisinvention are internal relative to a surface of the brain. By usingbrain scanning technology, such as MRI/fMRI that is able to makemeasurements from internally localized regions of the brain, the presentinvention is able to treat those internal localized regions of thebrain. Some other technologies are limited because their measurementsare made from surface points based upon current or voltage recorded atthe brain or scalp surface, or based upon radiation emitted from thebrain or scalp surface. A single signal emitted from any one localizedbrain region internal to the brain will propagate through the brainaccording to its conductivity to many points on the brain surface. Thissignal will be mixed with the signals from all other active brainregions as it propagates. Once mixed, this large number of competingsignals cannot be completely separated based upon a finite number ofsurface measurements. Some analysis methods have attempted 'sourceseparation approximations' to attempt to infer what point generated agiven signal in the presence of many other signals, but none cancompletely and definitively determine the signal from a particulardiscretely localized brain region due to the underlying physics of theproblem. This is based upon a limitation of the measurement technique:the electrical or radiation signal used to make the measurements iscontaminated by the tissue through which the signal must pass to enterand exit the brain between the transmitter and the receiver, and byadjacent tissue.

[0189] A major advance in measuring the activity in discretely localizedbrain regions was the advent of brain scanning technologies, such asfMRI, PET, and SPECT. These technologies overcome the obstacle ofmeasuring the activity in localized regions internal to the brainwithout substantial contamination from surrounding and interveningtissue. For example, an MRI/fMRI scanner uses a different magnetic fieldstrength at each point in space, which corresponds to a different RFcenter frequency for measurement. MRI/fMRI is therefore able to makemeasurements from only a single point (based upon field strength) byrecording RF at the relevant center frequency. This measurement is notsignificantly contaminated by activity from surrounding regions, or beregions between the point being measured and the surface of the brain.

[0190] By using brain scanning technology that can accurately measureinternal localized regions of the brain, the present invention is ableto monitor and treat internal, localized brain regions. This is animportant distinction from merely controlling activity in the brain as awhole, or in a large brain region as a whole. The brain is a structurewith hundreds of individual regions, some extremely small, and each withits own function. In order to control the brain's actions in ameaningfill way, it is important to spatially localize which regions aremeasured, which regions are activated, and which regions arede-activated. This invention allows the control of small, discretelylocalized brain regions. This invention also allows the control of thepattern of activity within a brain region to create a 2-D or 3-D patternof activation that can include sub-regions of increased activation andsub-regions of neutral or decreased activation.

[0191] This invention can employ measurements made using a scanningmethodology that records data from each point in a predefined volume. Inanother variation, the localized brain region that is monitoried is assmall as a single voxel. Taking measurements from a single point orsmall volume allows data collection to be concentrated on the singlevolume of measurement, rather than being divided across multiplemeasurement points across a larger volume. This also can obviate theneed for elements of the technology that enable scanning of themeasurement point.

[0192] The present invention may be applied to any disease or conditioninvolving inappropriate activity in one or more discretely localizedbrain region. For example, the present invention can be used to addressa decrease in activation of the substantia nigra that leads to adecrease in the release of the endogenous neuromodulator dopamine inParkinson's disease with resulting changes in activation in targetareas, the decrease in activation in the nucleus basalis of Meynert thatleads to a decrease in the release of the endogenous neuromodulatoracetylcholine to regulate the cerebral cortex in Alzheimer's disease, orthe decrease in frontal cortical activity in Major Depression that canbe positively impacted by increased release of the endogenousneuromodulator serotonin from serotonergic nuclei.

[0193] The present invention can also be applied to subject-specificconditions involving a decrease in activity within a particulardiscretely localized region, such as the decrease in activity in thestill-living tissue adjacent to tissue destroyed by ischemic braininjury (CVA/stroke).

[0194] Examples of regions of interest of the brain which may betargeted according to the present invention include, but are not limitedto those listed in FIG. 2.

[0195] The present invention is particularly well-suited for thetreatment of conditions that have a cause directly related to aninappropriate level or pattern of neural activation within a discretelylocalized brain region. This is because the invention utilizestechnology that allows these discretely localized brain regions to bedirectly spatially targeted, controlled, trained, and exercised.

[0196] The present invention is also particularly well-suited for thetreatment of conditions positively impacted by endogenousneuromodulatory compounds emanating from localized brain regions. Thisis because this invention allows the regions that produce or respond tothese compounds to be directly spatially targeted, controlled, trained,and exercised.

[0197] A feature of the methods, software and systems of the presentinvention is the communication to a subject through visual, auditory orother information, including measured information, instructions, orstimuli that are based upon the measured activity of discretelylocalized regions of his or her brain. This measurement can be basedupon substantially real time brain scanning technologies such asfunctional magnetic resonance imaging (fMRI) or other physiologicalmeasurement methods. By measuring physiological activity levels ofdiscretely localized regions of the brain and communicating instructionsor stimuli that are based upon those activity levels to the subject insubstantially real time, the subject is able to regulate, train, andexercise the physiological activity levels of those discretely localizedregions of the brain.

[0198] A further feature of the methods, software and systems of thepresent invention is the identification of certain training exercisesthat the subject can use to regulate the physiological activity levelsof those discretely localized regions of the brain. By first identifyingwhat training exercises are most effective for a selected localizedportion of a given subject's brain, the localized activation provided bythe present invention is enhanced. Furthermore, by then performing theselected training exercise where the subject's effectiveness inactivating the selected localized portion of the subject's brain ismonitored and communicated to the subject, the effectiveness of thetraining exercise is maintained and improved upon.

[0199] By performing the methods of the present invention, desiredlevels and patterns of physiological activation can be achieved withinregions of interest. Achievement of these levels and patterns can beused to achieve a variety of highly desirable results including, but notlimited to, the treatment of a number of conditions or psychiatric orneurologically-based diseases, improvement in performance or learning,and improvement of mood or affect. For example, the methods allowmonitoring and control over many aspects of neurological andpsychological disease, as well as improvements in mental performance andimprovement of psychological and emotional states and learning. Apartial list of diseases or conditions which may be addressed by thepresent invention include, but are not limited to Parkinson's disease,Alzheimer's disease, depression, psychosis, epilepsy, dementia,migraine, others described in FIG. 3, and those described in: Adams &Victor's Principles Of Neurology by Maurice Victor, Allan H. Ropper,Raymond D. Adams.

[0200] Different aspects of the present invention, including morespecific methods, software, and systems are provided herein. Thefollowing paragraphs provide an overview of an embodiment of trainingand exercise according to the invention. Further embodiments and detailsare provided in the sections that follow.

[0201] One step toward providing treatment using this invention is todetermine the primary region(s) of interest that mediates the conditionto be treated so that treatment can be focused upon this region ofinterest. An initial set of stimuli or instructions for behaviors may beselected that will selectively engage the brain region of interest, andthat may be used in training and exercise. It is also important tolocalize the region of interest within the brain of the subject usinganatomical and physiological scanning methods. Once the region ofinterest is localized for the subject, particular stimuli orinstructions for behaviors may be selected from the initially definedset to be used for training the subject The stimuli or instructions forbehaviors are typically selected that produce the highest level ofactivation of the brain region of interest during the particularstimulus or behavior.

[0202] At this point, training of the subject begins using the optimizedstimulus set. The subject takes part in multiple training trials intraining blocks. The training blocks take place within repeated or dailytraining sessions. The goal of the training is for the subject to gainincreased control over the region of interest, and to exercise thatregion to achieve greater activation. The exemplar stimuli/behaviorsisolate activation of particular brain regions, and the subject is giveninformation about the progress of their training.

[0203] For a particular training trial, while inside the scanningapparatus the subject is given the instruction to observe a particularstimulus or engage in a particular behavior. For example, the subjectreceives the instruction to make a particular movement of the hand. Theresultant activity level in the region of interest is measured by thescanning apparatus. This is analogous to an athlete lifting the weightson a particular weight-lifting machine using an isolated set of muscles.The subject is then given information about the activation that theywere able to achieve, analogous to an athlete observing how much weightthey were able to lift. Over training, the subject practices andexercises and gradually builds greater control and higher activation inthe region of interest. Training typically takes place over a number ofsessions on separate days. This training can be supplemented withadditional training outside of the scanner (when the subject would notreceive the information about their performance level) using theselected stimuli. The training can also be provided as an adjunct toadditional therapies such as pharmaceuticals or physical therapy.

[0204] Additional embodiments are described in the examples section.

[0205] The detailed discussion that follows through section 6 describesaspects of an embodiment of this invention that allows training andexercise of a subject for the purpose of treatment of a conditionthrough the regulation of certain brain regions.

[0206] 1. Determining a Treatment Method for a given Condition

[0207] This section describes a process by which treatment methods fordifferent conditions may be developed. It is noted that the subjectsreferred to in this section are not necessarily subjects that are beingtreated according to the present invention. Instead, the subjectsreferred to in this section are people who are used to evaluate how wellgiven stimuli, instructions for behaviors activate certain brainregions.

[0208] Developing treatment methods for different conditions may beperformed by evaluating a likely effectiveness of treating a givencondition by understanding whether there is an association between agiven condition and a particular brain region; determining the one ormore regions of interest to be trained for the given condition;determining one or more classes of exercises likely to engage thosebrain regions; determining a set of exemplar exercises from the one ormore classes for use in training; and testing the subject to ensure thatthe set of exemplar exercises are effective in activating the regions ofinterest.

[0209] A. Evaluating alikely effectiveness of Treating a given Condition

[0210] Numerous different conditions may benefit from training accordingto the present invention. For example, Parkinson's disease is causedlargely by insufficient activity of the brain's substantia nigra, andresultant patterns of activity in its neural target zones. The activityin the substantia nigra and its target zones can be increased throughtraining and exercise of this region of interest. In the case of stroke,regions adjacent to the zone destroyed by ischemia can be trained toachieve improvements in neural activation and regulation. Many otherexamples of conditions that may benefit from training according to thepresent invention are described in the Examples section herein.

[0211] The likelihood of success for a given condition to be treatedaccording to the present invention can be evaluated from knowledge ofthe etiology and variety of causal factors contributing to the conditionas understood at the time of treatment. More specifically, whenconsidering whether treatment will be effective for a given condition,attention should be given to whether the condition is related to brainactivity If there is a correlation between the presence of the conditionand a level or pattern of brain activity in one or more regions ofinterest, then, the methods of the present invention are likely capableof improving that condition by altering the level or pattern of brainactivity in the one or more particular brain regions.

[0212] B. Determining one or more Regions of Interest to be Trained forthe given Condition

[0213] As noted above, the brain comprises thousands of individualregions, each with its own function. Thus, in order to treat a givencondition, it is important to identify the one or more regions ofinterest associated with the condition. It should be noted that theprecise location of these regions can vary subject to subject. Hence, itis also important to identify the one or more regions of interest to betreated for a given subject. This ultimately makes the treatment methodsof the present invention highly individualized.

[0214] Determining the one or more discretely localized brain regions tobe trained for a given condition may be performed through a combinationof general knowledge about what regions are associated with the givencondition and thus need to be exercised, and information about theparticular subject.

[0215] For a given condition, the scientific and clinical literaturewill typically have information regarding which localized brain regionsare associated with the given condition. For example, the literature mayhave information associated with a given condition regarding human andanimal neural lesion data, pathology, histochemistry, pharmacology,brain stimulation studies, neural recording studies, and functional andanatomical imaging studies. Using this information, one is able to takea subject with a given condition, and determine which brain areas aremost relevant.

[0216] Once brain regions associated with a given condition areidentified in the abstract, it is important to then identify theseregions in a given subject's brain. It is noted that treatment will beperformed over a period of several days, weeks, month or even years.Therefore, it is advantageous to store information regarding thelocation of the relevant brain regions for a given once they areidentified so that less time and effort is needed to relocate them forsubsequent treatments.

[0217] In the case of fMRI scans, the regions of interest can either liewithin a single plane of section, or they can form contiguous ornon-contiguous volumes consisting of regions on multiple planes of asection. Software allows the definition of standard-sized regions ofinterest, centered on a location selected by the device operator orbased upon anatomical boundaries or measured physiological activationpatterns. Once particular regions of the brain are identified for agiven subject, the regions may be saved numerically to some form ofmemory (e.g., a computer disk) so they can be recalled for separatescanning runs, or for scans conducted in different sessions at laterdates.

[0218] C. Determining one or more Classes of Instructions or StimuliLikely to Engage the Brain Regions of Interest

[0219] Different regions of the brain are associated with differentfunctions, and may thereby be engaged and exercised by particular typesof stimuli, or by particular behaviors associated with those functions.Hence, by understanding what function a given region of the brainperforms, exercises can be designed which activate those brain regions.Through trial and error, exercises can be varied and thereby fine tunedboth with regard to their effectiveness in activating a given region ingeneral, and with regard to their effectiveness in activating a givenregion for a given subject.

[0220] Numerous physiological studies on many different brain regionshave been performed and have yielded a wealth of information regardingthe different kinds of stimuli or behaviors that can be used to engagedifferent specific brain regions. Many areas of the brain have alreadybeen ‘mapped’ in their functionality, in that particular zones areactivated by particular types of stimuli or behaviors, with adjacentzones activated by similar stimuli or behaviors. These types of studieshave allowed for the determination of what classes of stimulus orbehavior are likely to activate particular brain regions by selectingthe stimulus or behavior that are appropriate to the type of map and thepoint on the map being considered.

[0221] For example, countless detailed studies have determined frontalcortical regions that subserve movements, the motor cortex. Thus, alesion that partially inactivates the cortical hand representation willdestroy tissue engaged in hand movements. Adjacent tissue will beinvolved with the other hand, wrist, and arm movements. Therefore, inorder to treat the lesion, exercises to employ will include exercisesthat engage the brain region where the lesion is located as well asadjacent regions. In this instance, such exercises will likely encompassmovements of the relevant extremity, whether physically or mentalthoughts of their movement.

[0222] D. Determining a Set of Exemplar Instructions or Stimuli from theOne or More Classes of Examples

[0223] Once a general class of exercises has been determined for a givenregion of the brain, actual instances of specific stimuli or behaviorsare created that are able to exercise the brain region of interest.

[0224] The stimuli or instructions for behaviors to be used may becreated from within the class of stimuli or instructions for behaviorsthat will engage the brain region of interest. The exemplars created maybe real stimuli that will be presented to subjects, or real instructionsthat will lead the subject to engage in behaviors. These stimuli andinstructions may be created via computer to be presented digitally.Visual stimuli may be presented on a monitor viewed by the subject,auditory stimuli may be presented via speakers controlled by a computer,and tactile or other sensory stimuli may be presented viacomputer-controlled sensory stimulation devices as needed. For example,in order to engage certain regions of the temporal lobe involved in theprocessing of faces, a set of digitized photographic images of faces isused. In order to engage the primary motor cortical representation ofthe hand, a set of digitized images or movies depicting particular handmovements is uses. Typically, the stimuli to be presented can be basedon stimuli that have previously been demonstrated to be successful inactivating the brain region of interest.

[0225] Instructions can include text instructions that will inform thesubject of what to do and be presented either on the monitor, or theycan include verbal instructions presented via digital audio, or theinstructions can include icons or movies presented to the subject.

[0226] E. Testing Subjects to Ensure that the Set of ExemplarInstructions or Stimuli are Effective

[0227] In many instances, the process of creating stimuli orinstructions for behaviors is iterative, with the initial stimuli orinstructions for behaviors created needing to be fine-tuned. This may beperformed by first determining the appropriateness of the stimuli orinstructions for behaviors by testing them against subjects. It is notedthat this is an objective evaluation of the effectiveness of thebehavioral instructions or stimuli. This evaluation can be used for thesubject(s) with which it was determined, or for other subject(s).

[0228] Typically, the stimuli or instructions for behaviors arepresented in the context of a psychophysically controlled task ormeasurement or an operant conditioning task. The subject is asked todetect the stimuli or make discriminations among them when they arepresented using computer-controlled software, or asked to perform thebehaviors. This allows the stimuli or instructions for behaviors to beoptimized to be close to the subjects behavioral ability threshold, orability to detect or make discriminations among them. Stimuli are oftenselected that are slightly harder to detect or discriminate than thesubject can achieve, similar to what the subject can achieve, and easierthan what the subject can achieve. Supra-threshold stimuli can be usedas well to ensure the subject's success in detection or discrimination.Similarly with movements, cognitive, or other behaviors, behaviors areselected based upon a subject's ability to perform them up to a certainlevel of speed, accuracy, or performance ability.

[0229] The physiological responses for the stimuli selected can also beevaluated using pre-testing. In this case, the stimuli or instructionsfor behaviors are presented to subjects while the subjects are in ascanning apparatus, and tested for their efficacy in engaging theregions of interest. As will be described below for individual stimuliand instructions for behaviors, it is possible to determine which aremost effective and then ‘fine-tune’ to generate classes with the bestcharacteristics in terms of their ability to activate a given brainregion. As an example, flashed or reversing visual grating stimulusclasses can be optimized to have spacings between the gratings and flashrates that drive the largest physiological responses.

[0230] 2. Pre-Training a Subject

[0231] Once a treatment method has been determined for a particularcondition, as described in the preceding section, subjects with thatcondition may be treated. Prior to treatment, it is advantageous tofirst evaluate whether a particular subject is suitable for treatmentbased upon defined selection criteria; explain the training process indetail to the subject; and then pre-train the subject using a simulatedtraining environment.

[0232] A. Defining Subject Selection Criteria and Screening Subjects

[0233] It is desirable for the treatments of the present invention tohave a high frequency of success. It is therefore desirable to selectsubjects based upon the likelihood of their treatment being successful.

[0234] Examples of selection criteria that may be used include but arenot limited to:

[0235] 1) Whether the subject has the condition for which treatment isintended, based upon standard diagnostic criteria.

[0236] 2) Whether the subject has other, preferable treatment optionsavailable.

[0237] 3) Whether the subject has sufficient cognitive ability toparticipate in training.

[0238] 4) Whether the subject has any contraindication for brainscanning, such as phobias relating to being inside a scanner, orin-dwelling metal objects such as a pacemaker, or movement disordersthat would hinder the ability to make prolonged, stationary brain scans.

[0239] 5) Any indicators predictive of treatment success, such asprevious success of the method with subjects that are similar based upondiagnostic group or other signs and symptoms.

[0240] Each potential subject may be screened based upon some or all ofthese selection criteria to determine their suitability for treatment.

[0241] B. Subject Pretraining

[0242] It is advantageous to explain the training process to the subjectbefore training takes place in combination with a brain scanner tomeasure brain activity. Optionally, the subject is pre-trained using adevice that simulates the experiences that the subject will experiencewhen actual training is performed. This may include providing thesubject with the same or similar visual and auditory experiences thatwill later be provided. For example, when graphical interfaces are to beemployed, it may be desirable to pretrain a subject using thosegraphical interfaces, or at least show the subject the graphicalinterfaces he or she will see and explain their components.

[0243] The details and purpose of the training are explained to thesubject to allow him or her to be intimately familiar with what he orshe will be doing. A number of issues may be explained including: thatthe goal of training is for them to be able to increase the control overa particular brain region and then exercise the activation of thatregion; the importance of being still during the scanning session; theimportance of behaving in a similar way each trial and avoidingexcessive physiological activity such as deep sighs so that measurementsare consistent; the types of exercises that are likely to succeed inactivating the brain region of interest.

[0244] A subject may also be given detailed descriptions andexplanations of the functioning of the brain regions of interest; of themeasurement technology being used; of the timecourse of physiologicalactivity changes; of how to communicate with the controller; and so on.

[0245] A subject may also be trained regarding how to determine whatmental, perceptual or physical activities produce the greatest responsein the brain region(s) of interest by observing the information that heor she will receive regarding their activity metrics, and how togenerate mental, perceptual or physical activities that are likely toproduce the desired modulation.

[0246] The direction provided to a subject is important in the sensethat the subject is not asked to attempt to figure out how to increasethe level of physiological activity using any means he or she devises.Rather, it may be explained to the subject that their mental activitieslead to very specific patterns of brain activation, and that the goal isto find the activities that lead to the greatest pattern of activationin the region of interest, and then increase this level of activationthrough successive practice. It may also be explained to the subjectthat merely trying to increase the level of activity in a particularbrain region in a general way is unlikely to succeed, or will likelysucceed very slowly. Instead, it is by the activation of specificlocalized regions of the brain by carefully tailored exercises that theresults achievable by the present invention are provided.

[0247] It is also explained to the subject that neural responses arehighly variable, so it is important for them to repeat a given behaviora number of times and observe a number of the resultant responses to getan accurate sense of the response derived from that behavior. Inaddition, physiological responses may take some significant latency tobe measured after the subject initiates a behavior, such as up to 5-10seconds for some blood-flow-based measurements. Therefore, it isexplained to subjects that the relevant signal corresponding to a givenperception of a stimulus or performance of a behavior will only becomeapparent after a delay.

[0248] In regions where a clear behavioral strategy for controlling abrain region is not be determined in advance, but to be determinedduring the course of training, a subject should be instructed on how togo through a clear process of determining what behavioral strategyworks, and then refining it. This strategy is analogous to defining thetuning curve or optimal stimulus for a brain region, and involvesrepeatedly measuring the resultant activity from a broad range ofstimuli or behaviors in order to determine which ones lead to thelargest activation on average with some latency.

[0249] A subject is preferably pre-trained using exercises that closelymimic the exercises that will be performed when the brain activity isbeing measured. This allows the subject to become familiar with andpracticed on the exercises that he or she will be completing. Inaddition to ensuring that the subject has a clear understanding of whathe or she is to do, this allows any habituation of neural responses tothe training activities or other early learning effects to approachsteady-state.

[0250] A subject may also be trained using a simulation device thatmimics the user interface and training schedule and uses the sameselected stimuli that a subject would encounter during training in thescanning apparatus. This interface and its functioning will be describedin detail below.

[0251] In pre-training simulation, because brain activity is typicallynot being measured, the subject being trained to perform mentalexercises and observe stimuli is not given information regarding his orher patterns of neural activation that will otherwise be given duringactual training, as described below. Optionally, however, the subjectmay be given simulated patterns of neural activation, such as thosederived from past training sessions with the same or different subject,or using a random noise source or some other model of actual neuralactivity. The subject may also receive behavioral feedback alone, in theabsence of simulated neural feedback.

[0252] Overall, pre-training is typically preferably designed togenerate an experience as close as possible to the real training thatthe subject will undergo. Therefore, the training tasks that the subjectis asked to perform, the percent correct achieved, the displays that areprovided, stimuli that the subject experiences, and actions that thesubject undertakes are all preferably similar to those the subject willobserve when actual training is performed.

[0253] 3. Initial Brain Scanning Setup and Performing Scanning

[0254] Before beginning training using this invention, a number ofaspects of the invention must be prepared for use. These includepreparing the graphical user interface, preparing the subject within thescanning apparatus, and setting up for anatomical and physiologicalscanning. Section 3 lays out many of the aspects of what the inventiondoes in general, while describing the setup of the various components.In particular, it describes all of the computations that we can make,and the displays that we can generate. Later sections then tell us whatwe actually DO in training, and give detailed examples of thecomputations and displays.]

[0255] A. Preparation for Brain Scanning

[0256] Once a subject has been trained, the subject may be introducedinto a scanning apparatus where measurements of brain activity are takenand the location of targeted localized regions of the brain areidentified. This section describes this process in regard to a magneticresonance imaging scanner, such as a GE 3.0T Signa MRI scanner. How toperform analogous scanning using other instruments would be understoodby one of ordinary skill in the art.

[0257] i. Preparation of Subject within the Scanning Equipment

[0258] In order to take measurements of localized region of the brain,the subject of course has to be properly positioned relative to thescanner. Placement is made to ensure standard positioning, to helpensure that the subject has a positive and comfortable experience, andto ensure that the subject has access to visual and other stimuli aswell as output devices. The subject is ‘landmarked’ by measuring theposition of the nasion (bridge of the nose) using the scanner andsetting this to a standard zero position, from which measurements willbe taken. The subject's head is placed within a coil, such as adedicated head coil. The coil is selected to give the best signal fromthe region of interest. The subject is given earplugs or soundcancelling headphones to decrease noise within the scanner.Communication equipment may also be setup between the subject and thedevice operator or other healthcare professionals in attendance.

[0259] ii. Head Motion Stabilization and Physiological Gating

[0260] As would be expected, it is desirable that the subject's headremain perfectly stationary. In order to decrease head motion, thesubject may be placed within an adjustable or custom-made head motionstabilizer that is secured to the scanner. If additional motionstabilization is required, motion stabilization software, may be used tocorrect data volumes collected for movements of the subject within thescanner. An example of this software is described in CC Lee, et al.Real-time adaptive motion correction in functional MRI. Magn Reson Med1996;36:536-444. In instances where a structure is being measured thatis subject to significant physiological motion, the timing of initiationof successive measurements may also be triggered to correspond with aparticular phase of the cardiac or respiratory cycle according tostandard methods described in the literature.

[0261] iii. Brain Volume Registration

[0262] In order for the position of the head and the relatedmeasurements to be comparable from session to session, images andvolumes should be registered, allowing precise correspondence of voxelsacross days. This volume registration can have a manual component and anautomated component. In the manual component, the subject is positionedwithin the scanner in a stereotyped way to try to achieve similarplacement on successive occasions using a bitebar and fixed points ofreference within the scanning apparatus. Additionally, the zero pointfor scanning may set to the nasion of the subject (bridge of the nose)using a standard light beam approach built into the scanner. Finally,scanning sections are prescribed relative to fixed anatomical landmarkswithin the subject, including but not restricted to the anteriorcommissure, the posterior commissure, the mid-saggital line, the centralsulcus, the temporal pole, the calcarine fissure and pole, and thetopmost point on the cerebral cortex. If sections are prescribed inthree dimensions based upon the accurate positions of at least threeanatomical landmarks on the subject, then the positions of brain regionscan be reliably reproduced on successive sessions. Scanning sections canalso be prescribed relative to fiducial marks placed on the subjectusing material opaque to a scanning instrument. If these marks areplaced on known locations on the subject, then they can serve aslandmarks for scanning.

[0263] B. Anatomical Scanning

[0264] Anatomical scans of the subject may be made using an imagingapparatus to visualize internal brain structures. In one embodiment,detailed anatomical images are collected using an MRI scanner. In oneparticular example, whole-brain imaging data are acquired on a 3 TeslaMRI Signa LX Horizon Echospeed scanner (General Electric MedicalSystems, 8.2.5 system revisions) as described in the operatinginstructions for that instrument. For example, T1 and/or T2 weightedanatomical image data are collected from axial slices through the headwhich will be in substantial register with physiological data collectedlater. An embodiment collects 17 axial slices of 7 mm slice thickness,with each slice having a 256×256 voxel resolution over a 22 cm×22 cmarea, producing 256×256×17 voxel brain volume data. Higher resolutiondata may be collected as well to allow more detailed anatomicallocalization by changing the number of voxels in each of the threedimensions. MRI anatomical scanning methods are described in detail inneuroanatomical texts.

[0265] C. Physiological Scanning

[0266] An aspect of the present invention relates to the performance ofbrain scanning such that the physiological activity of regions ofinterest of the brain can be measured and monitored. It is noted thatsuch measurements and monitoring is preferably performed insubstantially real time so that computations can be performed andresultant information including measured information, stimuli, andinstructions can be frequently relayed to the subject in a timelyfashion to influence how the subject performs training exercises.

[0267] i. Measurements

[0268] Physiological activity measurement may take one or more ofseveral forms, including fMRI BOLD signals, fMRI EPI signals, PET orSPECT signals, or event-related signals conditioned on sensoryevents/motor behaviors, or other physiological measurements. Thesemeasurements may be made using a variety of physiological recordingapparatus. Examples of measurement apparati that may be used alone or incombination include, but are not limited to functional magneticresonance imaging (fMRI), PET, SPECT, EEG (electroencephalogram)recordings or event-related electrical potentials, MEG recordings(magnetoencephalogram), electrode-based electrophysiological recordingmethods including single-unit, multi-unit, field potential or evokedpotential recording, infrared or ultrasound based imaging methods, orother means of measuring physiological states and processes.

[0269] Functional magnetic resonance imaging (fMRI) is a particularexample of a brain scanning technology that is capable of measuring andmonitoring brain activity in substantially real time. fMRI is based uponchanges in Blood Oxygen Level Dependent (BOLD) contrast and providesspatially and temporally resolved visualization of the hemodynamicresponse evoked by neuronal activation. fMRI scanning can be performedaccording to widely published procedures. This technique has beendescribed in detail elsewhere including for example in Annu. Rev.Biomed. Eng. (2000) 2:633-660, the references included therein, and AnIntroduction to Functional Magnetic Resonance Imaging: Principles andTechniques by Richard B. Buxton (Hardcover—November 2001).

[0270] In one particular example, whole-brain imaging data may beacquired on a 3 Tesla MRI Signa LX Horizon Echospeed scanner (GeneralElectric Medical Systems, 8.2.5 system revisions) as described in theoperating instructions for that instrument. Functional images may beacquired in the same slices as previously collected anatomical images(see above) using T2*-sensitive gradient echo spiral pulse sequence (30ms TE; 1000 ms TR; 70 degree flip angle; 22 cm FOV; 64×64 acquisitionmatrix or similar parameters) See for example: Neuroimaging at 1.5 T and3.0 T: comparison of oxygenation-sensitive magnetic resonance imaging.G. Kr ger A. Kastrup G. H. Glover, Magn Reson Med. April, 2001;45(4):595-604; Three-dimensional spiral fMRI technique: a comparisonwith 2D spiral acquisition. S. Lai G. H. Glover, Magn Reson Med.January, 1998; 39(1):68-78. The physiological images collected areregistered with previously acquired anatomical images by lining theimages up voxel-for-voxel. A more thorough fMRI scanning protocol isprovided in Section 7 in the Examples.

[0271] It is noted that although many of the more detailed descriptionsprovided herein are directed to fMRI, it should be understood that thepresent invention may be used with any brain activity measurementtechnology that is capable of detecting activity in discretely localizedbrain regions. Over time, it is anticipated that new techniques will bedeveloped with the ability to detect activity in discretely localizedbrain regions. Furthermore existing measurement technologies may beadapted for detecting activity in discretely localized brain regions.All such measurement technologies, and their combinations, are intendedto be employable in conjunction with the present invention.

[0272] Once the scanning equipment is setup, physiological activation ofthe brain is measured. Generally, the process may comprise collectingscan data repeatedly (e.g. continuous collection at one scan persecond), reconstructing the raw physiological data into image data insubstantially real time, and performing computations on the resultantimages as depicted in FIG. 1.

[0273] Activity patterns may be measured within regions of interest orfor the whole brain, either at a point in time or continuously. This isachieved by scanning the imaging technology sequentially over a numberof voxels with some sampling rate, taking measurements from each one.This gives indications of the level of physiological activity at eachlocation at each point in time.

[0274] The number of different points that may be monitored willtypically decrease as the sampling rate is increased once theoperational limits of the equipment is reached. Therefore, it isfrequently necessary to specify the locations and sizes (in threedimensions) of the regions of interest to be monitored, as well as therate at which these regions of interest are to be sampled. These regionsof interest may form either a large and contiguous array (such as a cubecontaining a large number of contiguous voxels), or a number of discretelocations that are one or more voxel in size. The measured values usedfor the regions of interest can involve time or spatial averaging orother mathematical smoothing of data over a range of samples. In thisway, a vector of data may be acquired at each time point, and a largervector consisting of a time series of data may be collected.

[0275] In order to collect scan data, the functional scanning parametersare input. Preferably, the parameters are pre-set, for example usingcontrol software incorporated into the instrument Aside from inputtingthe functional scanning parameter, other things to check prior toinitiating scanning include: informing the subject that the scan isabout to begin, insuring that there is adequate data storage spaceavailable, and checking that all computer linkages are active.

[0276] ii. Scan Voxels, Scan Volumes, and Regions of Interest

[0277] As described in the definitions, a voxel refers to a point orthree dimensional volume from which one or more measurements are made.Using a suitable scanning methodology, measurements may be collectedfrom a large number of voxels. For example, measurements may be madefrom each component of a square grid volume of voxels corresponding to ascan volume. This scan volume may be positioned to include some or allof the brain of a subject. In this way, measurements may be made thatspan the entire brain, or a portion of the brain. Measurements may bemade for each voxel in the scan volume at every measurement time.Measurements may be repeated, such as once per second or at othersampling rates. This may produce a fill volume image of the activitylevel of each point in the brain each second.

[0278] In many instances, analyses according to present invention arebased on a particular subset of volumes from among the entire scanvolume. The particular subset of volumes may be theregion of interestfor that analysis.

[0279] A region of interest may include a selected one or more of thevoxels or measurement points. A region of interest may have a spatialshape and extent defined by the voxels that it includes within theentire scan volume. A typical region of interest may be a 5×5 voxelsquare array, or a 5×5×5 voxel cubic volume, centered on a selectedvoxel. A process for selecting a region of interest is described insection 4. Since a region of interest may be comprised of multiplevoxels from which independent activity measures are made, it may bepossible to measure either an aggregate average level of activity fromthe entire region of interest, or a spatial pattern of activitycomprising the activity at each voxel within the region of interest.

[0280] Measurement data may also be collected from a single voxel. Inthe case of collection of data from a single voxel, the one voxel maycorrespond to the region of interest.

[0281] D. Processing of Scan Data into Images and Metrics inSubstantially Real Time

[0282]FIG. 1 illustrates the process flow diagram for taking raw scandata and producing information that may be communicated to the subject.As illustrated in FIG. 1, raw scan data is converted to image/volumedata 125 corresponding to images and volumes of the brain by 3-Dimage/volume reconstruction software 120. These are referred to asimage/volume data, or as images/volumes, to connote the fact that eithera single planar image may be used, or a 3-D volume may be used. One ofthe simplest types of vector representation of physiological activationfor the images is a planar section of fMRI activity, taken with sometemporal resolution, and some spatial resolution. This provides a singleslice image of the state of activation of the brain at a particularinstant The resulting image/volume data 125 can then be used by the dataanalysis/behavioral control software 130, which is described in moredetail herein. The data analysis/behavioral control software 130generates information and selects stimuli or instructions to communicateto a subject 190 to influence how the subject performs trainingexercises. This takes place via three steps, each serving to generatethe input to the next: 1) pre-processing of data, 2) computation ofactivation image/volumes, 3) computation of activity metrics, 4)generation of information for the subject such as measured informationand selection of stimuli or instructions.

[0283] All of the computed values, such as those described in thissection, may be stored to computer memory or a computer storage devicefor later retrieval. This storage may take place each time computationsfor a given measurement time point are completed, or it may take placeat the end of a trial, or at the end of a training block or session. Inaddition, all of the computed values may be transmitted via the internetor other communication means at the time of computation, or at a latertime.

[0284] The process illustrated in FIG. 1 will now be described inrelation to processing fMRI data. It is noted that analogous dataprocessing may be performed for other data from other types ofinstrumentation. Detailed examples of processing that may be performedare provided in Examples section 1.

[0285] i. Scanner Software

[0286] Commercial data collection software 110 is available andtypically included with an MRI/fMRI scanner to control the process ofinitiating scanning pulse sequences, collecting measurements,communicating electronic signals associated with a scan, and producingraw scan data from the electronic signals. The raw data may be in theform of a k-space representation that can be accessed either fromcomputer memory or from a disk file. This representation must bereconstructed to produce a spatial representation of the signal, such asa scan image or volume.

[0287] ii. Reconstruction Software

[0288] Once the output raw data is formed from the data collectionsoftware 110, this data serves as the input to the 3-D image/volumereconstruction software 120. The 3-D image/volume reconstructionsoftware 120 performs computations upon this input that result in theoutput of 2-D scan images or 3-D scan volumes.

[0289] Converting the data to 2-D and 3-D scan images in substantiallyreal time may be performed using reconstruction software. Thereconstruction software may be conceptually similar to the software thatperforms offline k-space to volume reconstruction, with the distinctionthat it may run more efficiently and thus may be able to perform thenecessary calculations in substantially real time.

[0290] The reconstruction software 120 can take several forms, which arepublicly described and available. There is a substantially real timedata analysis package produced and commercially available from BrainInnovation, Inc. Maastricht, The Netherlands. There are many instancesof substantially real time reconstruction software described in theliterature, for example: Functional magnetic resonance imaging in realtime (FIRE): sliding- window correlation analysis and reference-vectoroptimization. D. Gembris J. G. Taylor S. Schor W. Frings D. Suter S.Posse. Magn Reson Med. February, 2000; 43(2):259-68; Goddard, N. H.,Hood G., Cohen, J. D., Eddy, W. F., Genovese, C. R., Noll, D. C. andNystrom, L. E., “Functional MRI Datatsets Analyzed Online”, in ParallelComputing for Industrial Applications, ed. A. Koniges (Morgan Kaufmann:in press)., Real-time image reconstruction for spiral MRI usingfixed-point calculation. J. R Liao IEEE Trans Med Imaging. July, 2000;19(7):690-8. Real-time interactive MR imaging system: sequenceoptimization, and basic and clinical evaluations. S. Naganawa T.Ishiguchi T. Ishigaki K. Sato T. Katagiri H. Kishimoto T. Mimura O.Takizawa C. Imura, Radiat Med. January, 2000; 18(1):71-9. Real-time 3Dimage registration for functional MRI. R. W. Cox A. Jesmanowicz. MagnReson Med. December, 1999; 42(6):1014-8. Fast “real time” imaging withdifferent k-space update strategies for interventional procedures. M.Busch A. Bomstedt M. Wendt J. L. Duerk J. S. Lewin D. Gronemeyer J MagnReson Imaging. January, 1998; 8(4):944-54.

[0291] In one embodiment, the process of taking the data and convertingit to 2-D and 3-D scan images is performed one or more times every 10seconds, optionally at least every 5, 4, 2, 1, 0.5, 0.2, 0.1, 0.01seconds which is referred to herein as “substantially real time.” Thisallows the scan images and/or information garnered from the scan imagesto be processed, with the results communicated to the subject toinfluence how the subject performs training exercises. It is noted thatas processor speed continues to improve, and more efficient software isdeveloped, faster and faster turn around times will be made possible andmay be performed by the present invention.

[0292] In one embodiment, the resulting output image files from thetransformations are flat, header-less files containing 64×64×17 2 byteintegers corresponding to values for the voxels for each scan volume.The output image/volume data from the reconstruction software is thenpassed as one input to the analysis and control software.

[0293] iii. Pre-Processing of Image/Volume Data

[0294] One function that the data analysis/behavioral control software130 may perform is to pre-process 135 the input data. It is noted thatthe software may optionally process the input data withoutpreprocessing.

[0295] Once optionally pre-processed, the data may be used to computeactivity metrics from image or volume data. These activity metrics maythenbe used to generate information to present to the subject, and makeselections of stimuli or instructions.

[0296] The output images generated by the 3-D image/volumereconstruction software 120 are typically transferred to a separatecomputer that contains the data analysis/behavioral control software130. Because it is desirable to relay information to the subject as soonafter brain scan measurements are taken, this transfer preferably takesplace by reading the stored data files containing individual scanvolumes from the reconstruction computer using an NFS protocol. Theformat of these data are transformed if necessary to allow compatibilitybetween computers, and they are read into memory by the dataanalysis/behavioral control software 130 on the substantially real timecontrol computer in substantially real time. This process can also takeplace on a single computer if it has sufficient processing power.

[0297] Many types of pre-processing of image/volume data are available,and examples are described in detail in Examples section 1.A. As oneexample embodiment, the images may be simply spatially smoothed byconvolving each image with a 2-D gaussian filter with a 1 pixel halfwidth. The output of the pre-processing step is an image or volume ofpre-processed data at every data collection time. This is similar inform to the input to this step, but transformed by the pre-processingcomputations.

[0298] iv. Computation of Activation Images/Volumes

[0299] Taking the images/volumes as input, optionally after they havebeen pre-processed, the next step is to compute activationimages/volumes This is typically performed by the dataanalysis/behavioral control software 130. Many types of activationimages/volumes can be computed, and examples are described in detail inExamples section 1.B. below. These activation images/volumes can be usedfirst to determine the location of a region of interest for a particularsubject, and later as the input for making measurements from this regionof interest.

[0300] An example activation volume that may be computed for the purposeof determining the location of the region of interest in a subject is a%BOLD difference image, computed taking preprocessed scan volumes asinput by taking the value at each voxel from scan data at the currenttime and subtracting the value for an early slice, for example the5^(th) scan volume collected. This result is then divided by the valueat the early slice, for example the 5^(th) scan volume, and multipliedby 100%. The result is a %BOLD difference image that indicates the levelof activation relative to the early scan volume.

[0301] v. Computation of Activity Metrics

[0302] Once activation images/volumes have been computed, it is possibleto use these as inputs to the computation of activity metrics. Thisprocess involves computations of values from a defined region on theactivation images/volumes that have been measured. Many types ofactivity metrics can be computed, and examples are described in detailin Examples section 1.C. below. For example, an average value of theactivation for all of the voxels within a region of interest may becomputed. In this case, the activation volume data for each voxel in adefined region of interest at each time point are used as input, and anaverage value of the activation is calculated for each time point forthe group of voxels. This average may then be displayed to the subjector device operator using a graphical user interface described in thenext sections.

[0303] E. Setup of Graphical User Interface

[0304] An important aspect of the present invention relates to employingmeasured brain activity to provide measured information, stimuli, orinstructions to subjects that may be used to influence how the subjectperforms training exercises. This influence may be provided by havingthe subject interact with devices designed to be used in combinationwith this invention. A variety of interaction mechanisms are envisioned,some of which are described in detail in the examples section. Otherswill be appreciated by one of ordinary skill.

[0305] One primary type of display that may be presented to a subject ordevice operator in substantially real time include measures ofphysiological activity such as activation maps of the subject's brainactivity, activity metrics from localized brain regions. Another primarytype of display is stimuli that the subject will perceive that may beuseful in activating certain brain regions and performing training.Another type of display may be instructions to the subject. The setup ofthe user interface and its potential components are described in thefollowing sections.

[0306] i. Presenting an Overall User Interface to the Subject and DeviceOperator

[0307] In one embodiment, as shown in FIG. 4, a subject 200 viewsinformation such as measured information, stimuli, or instructions usingviewing goggles 210, such as virtual reality goggles, controlled by acomputer 220 connected by a cable 225, while the subject is inside thebore of a scanning apparatus 230. Viewing goggles for the purpose aremanufactured by Resonance Technology, Inc, California. The deviceoperator may view a similar screen on a second display. In addition, aremote participant may view a similar display on a remote displayscreen. Information for remote displaying may be conveyedelectronically, for example using a wire, wireless, or internetconnection. The display presented for the device operator may beseparately configurable to contain a different set of panels than thatdisplayed to the subject.

[0308] In another embodiment, the subject 200, views and image displayedon a display 240 and projected through a lens 250 onto areverse-projection screen 260. The subject views the screen through amirror 270.

[0309] Using some form of display, the subject views instructions ofwhat the subject is to do, information indicating the physiologicalactivation of the subject's brain in substantially real time, indicatorsof the subject's success and progress in training, and/or other forms ofinformation such as the number of trials remaining in a trainingsession.

[0310] A variety of types of information and display screens can bepresented. For example, visual stimuli may be presented to the subjectvia some form of display. FIG. 4 illustrates one such display system.When the subject sees the stimuli, associated changes in the brain ofthe subject will be observed. The many types of information that may bedisplayed are described below after the information that they willcontain has been described.

[0311] Auditory stimuli may also be presented to the subject, such asdigitized speech, tones, music, or other types of sounds. Auditorystimuli may be presented to the subject via some form of speaker system,optionally worn by the subject. Tactile stimuli may be presented using atactile stimulation apparatus such as a Chubbock stimulator or othertactile stimulator as described in: A tactile air stimulator for humans.E. W. Wineman, Psychophysiology. November, 1971; 8(6):787-9. Temperaturestimuli may be presented using skin heating or cooling probes. Olfactorystimuli may be communicated using a device designed to present gaseousodors to the subject in the scanner, as for example described in: Timecourse of odorant-induced activation in the human primary olfactorycortex. N. Sobel V. Prabhakaran Z. Zhao J. E. Desmond G. H. Glover E. V.Sullivan J. D. Gabrieli J Neurophysiol. January, 2000; 83(1):537-51.When the subject receives any of these stimuli, associated changes inthe brain of the subject may be observed. These changes may then bemeasured as has been described.

[0312] ii. User Interface Screens

[0313] The subject and device operator may view a display a screen 9000depicted in FIG. 5. This screen may contain a large variety of elementsthat can be selected for display, or hidden from view, and may each beappropriately sized to be visible in adequate detail. The screen maycontain a sector panel 9100 that contains a list or set of graphicalicons representing the other panels that may be displayed. Both thedevice operator and the subject are able to make selections from thisselector panel 9100 using a pointing device such as a mouse. When apanel has been selected, it becomes visible on the screen, and thesubject or device operator can use the pointing device to select theposition and size of the panel on the screen. The user may select one ormore of each type of panel to display. In some cases, the same type ofpanel may be displayed more than once for different purposes, such asthe use of two anatomy panels, one to show a coronal section, and one anaxial section.

[0314] iii. Presenting Images and Information

[0315] Data obtained and processed from an fMRI or another physiologicalactivity measurement apparatus may be presented in substantially realtime either to the subject of whom the fMRI scan is being taken, to thedevice operator, and/or another professional that is present, such as adoctor, nurse, technician.

[0316] The information displayed can include anatomical brain images, aswell as physiological activation images/volumes, and activity metrics.The results of all of the computations described in section 3.D. abovemay be used as input to present image and metric data to the subject ordevice operator. One skilled in the art will recognize possible modes ofdisplay for each of the types of computed information described.

[0317]FIG. 5 shows several examples of the presentation of image andmetric data, such as several activity metrics from the region ofinterest 9600, an alternate region of interest 9700 and the difference9800, a PETH from the ROI averaged over several trials 9900, and aphysiological correlation map 9950 indicating the brain areas activatedby a trial and showing the region of interest.

[0318] These display may all be used to inform a subject of theirphysiological activation. This information can be used by subject whilethey are still in the measurement device to guide their performance ortraining. As subjects view the level of activation caused by particularstrategies, stimuli, or behaviors, they can select how to behaviorduring the current trial or on forthcoming trials to improve theirperformance.

[0319] Further detailed examples of the types of information that may bepresented and their uses are described in Examples sections 1, 2 and 3.

[0320] iv. Displaying Information and Instructions

[0321] In order to influence a subjects performance of trials andtraining, information may also be presented via a display, such asmeasured information, stimuli, or instructions. This information mayinclude indications of the subjects success in training or performancetargets. This display may also include instructions for the subject,such as to undertake a particular type of trial, or achieve a particularperformance target. FIG. 5 illustrates a video instruction for a subjectto make an indicated movement 9200, and a success analogy indicating tothe subject the level of activation achieved in a brain area beingexercised in the form of a visual analogy.

[0322] Again, detailed examples of the types of information that may bepresented are described in Examples sections 1,2 and 3.

[0323] 4. Localizing Brain Regions of Interest in a Subject

[0324] In order to select the area on which measurements may be focuses,different methods may be used to localize a region of interest. Thesemethods include anatomical methods for localizing structures, andphysiological methods for determining volume activated by a givenstimulus or behavior. A region of interest normally corresponds to asubset of the full scan volume that may be collected at each measurementtime point. These voxels are selected because of their importance inmeasurement or training. The voxels within a region of interest may bedefined in a number of ways. They may be defined to be within theanatomical boundaries of one or more brain regions as determined throughanatomical scans. They may be defined by the fact that they areactivated in correlation with a stimulus, behavior or task. They may bedefined arbitrarily by the device operator using a selection screen thatallows the device operator to select individual voxels or regions ofinterest. They may be defined stereotaxically or by adjusting theposition of the patient within the measurement apparatus in such a waythat the apparatus measures activation from a defined point or areawithin the subject. The primary region of interest is normally the areathat is being trained, and that the subject is attempting to modulateactivation within. Comparison regions of interest are other definedregions that may be compared with the primary region of interest, suchas other parts of the brain that are not intended to be activated by thetask. A region of interest or volume of interest need not be spatiallycontiguous. For instance, a region of interest might constitute thesubstantia nigra and sub-thalamic nucleus on both sides of the brain,four non-spatially-contiguous volumes.

[0325] A. Anatomical Localization of Brain Regions of Interest

[0326] Once anatomical data has been collected for a subject,anatomically defined brain regions may be localized for the subject withreference to the collected anatomical information using either referenceto a standard anatomical atlas, or using a manual search. In eithercase, positions are measured relative to brain landmarks such as theanterior and posterior commissures, and/or fiducial marks placed ondefined locations on the subject using scanner-opaque materials.

[0327] To use manual search for a structure, the operator can viewsections through the 3-D voxel data and search for known brainanatomical structures using radiological knowledge to locate the desiredbrain regions. The operator can then select combinations of individualvoxels using a pointing device, or areas using a bounding line or shape.These selected voxels can be saved in computer memory, as well as savedto disk memory and recalled on later occasions.

[0328] Preferably, the software used in combination with the brainimaging device converts the anatomical data to a form that may bedisplayed or otherwise communicated to the subject or device operator insubstantially real time, preferably while the subject is within thescanner. This allows the subject or device operator to use thisinformation to select regions of interest for training, or to influencehow the subject is performing his or her training exercises.

[0329] In one variation, software is employed that makes a 3-Dtransformation from standard space to the space of the subject's brain,and back, in substantially real time. For example, the software may takeas input a set of 3-D Talairach coordinates or an anatomical volumedirectly from a computer-generated brain atlas and spatially transformthe coordinates according to a 3-D spatial mapping to yield thecorresponding locations within the anatomical volume measured for thesubject.

[0330] Another example of defining a region of interest anatomically isto use a defined anatomical region from a reference brain such as inTalairach or MNI (Montreal Neurological Institute) coordinates. In thiscase, the anatomical region is defined in the standard coordinates, andthen spatially transformed to localize the voxels corresponding to theanatomical structure in the subjects brain. This process is described infurther detail at Section 23D in the Examples.

[0331] B. Physiological Localization of Brain Regions of Interest

[0332] The one or more discretely localized regions of the brain thatwill define the region of interest that may be used for training may bedefined physiologically through finding the voxels that are modulated byone or more stimulus or behavior in comparison with a backgroundcondition. In order to do this, an important aspect of the presentinvention is its ability to monitor physiological activity insubstantially real time after the stimulus or instruction for a behavioris provided so that the effect that the stimulus or behavior had onactivity can be accurately determined. In addition, the brain region ofinterest may be determined within a short period of time after thecollection of the physiological data. This short period of time may beless than 10, 5, 2, 1, 0.5, 0.25, 0.01 or less minutes.

[0333] Defining the region of interest may be performed by having thesubject take part in a set of physiological ROI localization trials.During these trials, the subject engages in behaviors or experiencesstimuli that are intended to activate one or more region(s) of interest.By monitoring resultant physiological activity, the location of theseone or more region(s) are identified for that subject. The region ofinterest is normally defined after the completion of these trials basedupon the voxels that are modulated. However, it is also possible todefine the region of interest before all of the trials are complete, andthen iteratively redefine the region of interest as additionalsubstantially real time based measurements are taken.

[0334] Regions or volumes of interest may be defined that are modulatedby the stimulus or behavior condition, and this determination can bemade while the subject is inside the scanning apparatus. Regions ofinterest may either be defined on a voxel-by-voxel basis, or by defininga circumscribed area or volume such as a rectangle, circle, cube, orspheroid. The defining characteristic for whether each voxel will bewithin a region of interest may be based upon the value of an activationimage/volume at the corresponding voxel. If the voxel is above a definedthreshold in the activation image/volume, then the voxel is included inthe region of interest. This process can take place either manually, orin a fully or partially automated fashion as described in the followingtwo sections

[0335] i. Example of Presentation of Physiological Localization Trials

[0336] The following example illustrates how a physiologicallocalization trial may be performed. It should be noted that theparticular physiological localization trial to be used will vary withthe subject, the condition to be addressed, and hence the regions of thebrain implicated.

[0337] In this example, in order to measure the modulation, a stimulusor behavior condition is presented to the subject following a rest orbackground period to constitute a physiological localization trial.These trials may be repeated one or more times. Measurements are made ofthe resultant physiological activation patterns in the brain scan volumeat multiple time points throughout the localization trials. In order tolocalize the primary motor cortical representation of the hand, asubject may be asked to alternate between 30 second periods of rest with30 second periods of moving, or imagining moving, the index finger ofthe right hand while scanning of the T2* weighted activation level ismeasured at every voxel within a brain scan volume every second.

[0338] ii. Manual Physiological Definition of Region of Interest

[0339] Once data has been collected, a region of interest may bedetermined from physiological localization trials, one or more regionswithin the brain that are selectively activated during one portion ofthe trials may be determined. For example, if the trials contain a restperiod and a task period, a region may be determined which is activatedselectively during the task period compared to the rest period. Thisprocess may take place using a principally manual method whereby thesubject or device operator selects groups of voxels with strongmodulation, any may view data corresponding to the time course ofactivation of these selected groups of voxels. Alternatively, thisprocess may be partially or fully automated, with software selecting aset of voxels that meet certain criteria, such as a threshold level ofmodulation.

[0340] A wide variety of different physiological activation maps may becomputed, as described in section 3.D. In one example, thesephysiological activation maps may then be used to compute regions ofinterest through a manual process of selecting the voxels that areactivated by a portion of a trial using a provided display screen. Forexample, the average value during the stimulus or behavior conditionminus the average value during the background or rest condition may becomputed for each voxel in a scan volume. A montage for thephysiological localization of an ROI 8000 using color coded activationmaps may be presented to the subject as depicted in FIG. 6 on the userinterface 8001. This figure represents actual data collected from asubject in substantially real time, collected using a task involvingmental rehearsal of an imagined motion of the second digit of the righthand. This data could be used to select a region of interest while thesubject is in the scanner. In addition, each panel of the display maycontain a scale 8020, and a numerical index for the scale 8030 that mayinclude measurement units. The subject or device operator may view eachplanar section within the scan volume in any plane of section, showingthe level of the activation map. The corresponding anatomical sectionmay be presented as well. The subject or device operator may use apointing device such as a mouse to indicate the position of a region ofinterest 8050 based upon the area(s) that show activation on one or moreof the sections shown. The subject or device operator may also zoom inor out on any section to more accurately localize are area ofactivation.

[0341] At this point, activity metrics are computed for this selectedarea or volume, and results may be displayed substantially immediately.This process may take place in a limited period of time. This period oftime may be within 10, 5, 2, 1, 0.5, 0.25, 0.1, 0.01 or less secondsfrom the time of collection of the data. This process may take placewhile the subject is still in the measurement apparatus, such as thescanner. This process may take place prior to training of the subject.The timecourse of the average activity for this bounded area is computedand displayed 8100, as well as the PETH for this area triggered on thebeginning of each 30 second rest period 8200. Each of these may bedisplayed with their corresponding timescale and magnitude scale, andmay additionally include standard error or standard deviation measures,with an example shown for the PETH. The operator can then accept theselected area of the given section as the region of interest, or repeatthe process until he or she is satisfied with the region of interestthat has been selected.

[0342] iii. Automated Physiological Definition of Region of Interest

[0343] Regions of interest can be defined automatically using numericalcriteria based upon the voxels of a scan volume, or a sub-region of ascan volume. These automatically defined regions of interest can then bepresented to the subject or device operator for acceptance oralteration. This process may take place in substantially real time, andmay take place while the subject is still in the measurement apparatus.

[0344] Numerical criteria based upon the computed activationimages/volumes can be used to determine whether individual voxels are tobe included within a region of interest. In one embodiment, the processinvolves performing a number of physiological localization trials, andprocessing the resulting scan volume data into activation maps. The scanvolumes may be pre-processed, and activation images/volumes may bedefined. These activation images/volumes may be thresholded to selectrelevant voxels to be included in the region of interest. Additionally,spatial grouping may be employed, such as to reject voxels that are notadjacent to other selected voxels.

[0345] In one example, the 30 second rest, 30 second index fingermovement task is used. Pre-processing uses a 1 pixel gaussian spatialfilter using methods as described in Examples section 1. %BOLDdifference activation volumes may be computed that correspond to:100%×(the average computed for each voxel for all scan volumes fromperiods starting within 5 seconds after the start of behavior until theend of behavior, minus the average computed for each voxel for all scanvolumes from periods starting within 5 seconds after the start of restuntil the end of rest) divided by the average computed for each voxelfor all scan volumes from periods starting within 5 seconds after thestart of rest until the end of rest. This leads to a % difference map.The voxels with large values may be the voxels that are positivelyactivated by this task, and may include the motor cortical regions thatsubserve this task. A region of interest may then be defined using adifference criterion such as all voxels with a difference value above acertain criterion, such as 0.5%. Voxels may be further selected bydisregarding all voxels further than a criterion distance, for exampleone voxel, from a criterion number of other voxels above the threshold,such as one voxel.

[0346] One criterion used for automated physiological definition of aregion of interest is a difference criterion, such as the averagedifference in %BOLD activation level between the stimulus or behaviorcondition and background, as just described. Another criterion used forautomated physiological definition of a region of interest is at-statistic criterion, such as a t-test statistical contrast comparingvoxel values during a stimulus and a rest condition. Another criterionused for automated physiological definition of a region of interest is astatistical criterion, such as a an F-test statistical contrastcomparing voxel values during a stimulus and a rest condition. Anothercriterion used for automated physiological definition of a region ofinterest is a correlation, such as the correlation of the activation ofa voxel with the stimulus or behavior condition across repeated trials.Another criterion used for automated physiological definition of aregion of interest is an additional statistical measure, such as thegeneral liner model, non-parametric statistics, and corrections forrepeated measures and spatial features as described in the documentationof existing MRI/fMRI/PET data processing packages. Another criterionused for automated physiological definition of a region of interest maybe any of those described for the computation of activation maps oractivity metrics in Examples section 1.

[0347] Once an ROI has been automatically determine, it can be analyzedjust as with a described for a manually determined ROI in section iiabove. The timecourse of the average activity for this bounded area maybe computed and displayed, as well as the PETH for this area triggeredon the beginning of each 30 second rest period. The operator may thenaccept the selected area, modify it by adding or removing voxels orareas, or repeat the process until he or she is satisfied with theregion of interest that has been selected. This allows the user toselect regions until the region that is most strongly activated by thestimulus has been determined.

[0348] 5. Determining a Set of Effective Stimuli or Behaviors for aParticular Subject

[0349] Once the region of interest has been identified, stimuli orbehaviors may be evaluated while monitoring the physiological activityresponse in the region of interest in order to determine stimuli orbehaviors that are effective and relatively more effective in alteringthe physiological activity of the region of interest.

[0350] It is important to note that stimuli or behaviors that areeffective for altering the physiological activity of a given region ofinterest for a first subject may not also be effective for a second,different subject. Hence, the present invention contemplates that thestimuli or behaviors used to alter the physiological activity of theregion of interest should be individualized for a given subject.Described herein is an evaluation of the stimuli or instructions forbehavior for an individual subject in order to select the most effectivestimuli or instructions for behavior for that subject. It should benoted that the step described in section 5 of selecting the mosteffective stimuli or instructions for behavior for that subject isoptional, and may also not be carried out, instead using the effectivestimulus set described in section 1.E.

[0351] Determining effective and more effective stimuli or behaviors maybe performed by presenting a series of different stimuli or instructionsfor behavior from a set of exemplars one or more times, determining anactivity measure or index for each different stimulus or behavior fromone or more brain regions of interest, comparing the effect eachdifferent stimulus or behavior had, and selecting the one or morestimuli or instructions for behavior that had the most desired affect onactivity. By performing this selection process, the most effectivestimuli or instructions for behavior may be identified for a givenregion of interest for a given subject.

[0352] Described below is an example of a process that may be used todetermine a set of effective stimuli or instructions for behavior.

[0353] The subject may be in an fMRI scanner as described, andphysiological measurements may be conducted repeatedly throughout tomeasure scan volumes. A series of trials may be conducted, each trialconsisting of a 30 second rest or background period, followed by a 30second period of activation by a behavior. For each trial, first thesubject is initially allowed to rest for 30 seconds. A stimulus orinstruction for behavior is then selected. This selection may be arandom selection. Additional selection methods are described in Examplessection 3 below. The selected stimulus or instruction for behaviorcondition is then employed. Optionally, this includes presenting thestimulus or instruction to the subject using a subject user interface,such as a display that can be viewed by the subject. The activation forthe selected stimulus or behavior may then measured as the %BOLDdifference in average activity within a region of interest during thestimulus or behavior compared with during the rest period.

[0354] This process is repeated for different stimuli or instructionsfor behavior until all the stimuli or instructions for behavior to beevaluated have been presented, or until stimuli or instructions forbehavior have been identified that provide a desired level ofactivation. The stopping point can optionally be defined by a selectednumber of repetitions of each condition, or a variance-based measure ofcertainty regarding the response to each stimulus or instruction forbehavior, such as the certainty of a maximum likelihood measure of themost effective stimulus or instruction for behavior.

[0355] Based upon the activation patterns observed for each stimulus orinstruction for behavior, certain stimuli or instructions for behaviorare selected to be used in training. This selection is typically made byselecting a small number of stimuli or instructions for behavior fromthe complete set that elicit the largest activation in the region ofinterest. The more effective stimuli or instructions for behaviors arethen used as the training exercises for the subject.

[0356] 6. Training of a Subject

[0357] The invention disclosed may be used for training subjects, suchas the training of subjects to modulate selected brain regions. Once abrain region of interest has been localized and effective stimuli orinstructions for behavior have been selected based upon their ability tomodulate the brain regions of interest, these stimuli or instructionsfor behavior may be used to train the subject.

[0358] Training may comprise performing trials comprised of alternatingperiods of rest, followed by exercise. These trials may be designed toengage the regions of interest of the brain using the selected set ofeffective stimuli or instructions for behavior. These alternatingperiods of rest and performing a task are typically formed together intotraining blocks that last at least 1, 5, 10, 20, 30 or more minutes,with physiological scanning beginning at the start of a training block,and taking place during each training block. Training blocks may beperiodically repeated, with 1-10 training blocks taking place in onetraining session, and multiple training sessions taking place on thesame day or on different days. The progress and physiology of thesubject may be measured frequently and preferably in substantially realtime during the training block.

[0359] As discussed, measurements of physiological activity,computations of results, and display of information are preferablyperformed in substantially real time. This display of information may beused by the subject to guide their performance and/or training strategy.For example, the subject may use the display to determine whichperformance strategies are most effective, and continue to use thesestrategies in favor of others. This display of information may be usedby the device operator to make selections of how training shouldproceed, such as selecting stimuli for training.

[0360] In some ‘control’ trials the subject may not be provided withinformation about his or her brain activity, or may be provided withsham information based on random fluctuations or information from adifferent brain region or a previous time. These trials allow anestimate of the performance that the subject can achieve within thepresence of the scanning information. These trials will be describedseparately in section 6.G. below.

[0361] Data from subject training is preferably recorded and stored.This allows the progress of the subject to be monitored and relayed tothe operator and/or the subject. For example, a common type ofinformation that may be relayed is an average level of the activitymetric for the region of interest that the subject was able to achieveduring each training trial, training block, and training session. Thisinformation may also be recorded to a more permanent recording medium,such as a computer disk storage device. Any and all raw data andcomputed measures may be stored for later recall.

[0362] A. Conducting Trials

[0363] During training, subjects may participate in a series of trainingtrials, and physiological measurements may be made repeatedly at fixedintervals throughout. Training may also take place in the absence ofphysiological measurement as described in section 6.J. During a trial,the subject may first be allowed to rest for a period of time, astimulus or behavior may be selected to activate the particular regionof interest, and the subject may then be asked to attempt to activate aregion of interest using the stimulus or behavior selected. Themeasurements taken during rest provide a baseline so that the effect thestimulus or behavior has can be better measured. It is noted that therest measurement can precede or follow the measurement associated withthe stimulus or behavior.

[0364] As an example, a behavioral trial within an fMRI scanner mayconsist of the subject first resting, and then attempting to activate aselected region of interest by observing stimuli and engaging inbehaviors that will activate that region, such as imagining the motionof the right hand. The trial may begin with the presentation of aninstruction for the subject to rest for a period of time. The stimulusor behavior that will be used in the trial may then be selected by theanalysis and control software and then presented to the subject, such asan instruction to imagine moving the second digit of the right hand.This instruction may lead the subject to begin an exercise using anystimuli necessary to conduct the exercise. The subject may then performthe exercise, typically for a 30 second or 1 minute period of time. Inthis example, the subject may imagine making a hand movement in order toactivate a motor cortical region. In training designed to activate adifferent brain region, the subject might be instructed to view orimagine a particular face to activate a face-selective brain region, orengage in a sensory discrimination test to activate a sensory region.After performing the exercise, the subject is again allowed to rest.After the rest, the subject may be asked to respond to a question insome cases, such as selecting whether a stimulus presented in the trialcontained a particular feature. The training trial may then be repeatedmultiple times during the training block.

[0365] Some aspects of this process are explained in further detail inthe following sections.

[0366] B. Measuring and Displaying of Physiological Activity

[0367] Substantially throughout the process of training, the physiologyof the subject may be measured in the scanner. This information may bepresented to the subject and the device operator, and may also be usedfor additional computations such as the computation of metrics from aregion of interest. This process takes place at a regular repetitionrate, such as one set of measurements per second in one example, or atan alternate sampling rate.

[0368] i. Physiological Measurement

[0369] While the subject engages in training, data are acquired andprocessed about the resultant brain activation. This process has beendescribed above in sections 3.D. and 3.E. and FIG. 1. In summary, thisprocess may comprise:

[0370] collecting raw data as described in section 3.D.i

[0371] reconstructing the result into images/volumes as described insection 3.D.ii.

[0372] pre-processing the result as described in section 3.D.iii.

[0373] computing activation images/volumes from the result as describedin section 3.D.iv.

[0374] computation of activity metrics from the result for definedregion(s) of interest as described in section 3.D.v.

[0375] ii. Displaying Physiological Activation Maps

[0376] Many varieties of measurements may be made, and resultantcomputations performed and results displayed. Once activationimages/volumes and activity metrics have been computed, they may bedisplayed to the subject and/or the device operator, or to remoteparties. As shown in FIG. 1, the data analysis/behavioral controlsoftware 130 can provide information, such as measured information,stimuli, or instructions of various types on the display 180 viewed bythe subject 190. This display can include physiological images of thesubject's brain, matched anatomical images at the same level of section,3-D reconstructions of either anatomy or physiological activationpatterns, and both difference activity level images and statisticalmaps. The device operator and subject can therefore observe the patternof activation as it evolves on pseudo-colored images. This sectiondescribes one example of information displayed. Further detailedexamples of displays are described in examples sections 1 and 2.

[0377] In one example, the T2* weighted activation is measured in a64×64×17 voxel scan volume corresponding to a 22×22×12 cm volume of asubject's brain. The subject engages in training involving a repeatedtask of 30s rest and then 30s imagined finger motion. Data are convertedinto scan volumes once per second in a process requiring less than onesecond. In this example, no pre-processing is used of the scan volumesgenerated. Scan volumes may be turned into %BOLD difference activationvolumes by taking each successive volume, subtracting the 5^(th) volumerecorded, dividing the result by the 5^(th) volume, and multiplying by100% to yield an activation volume. The 5 volume is used as by 5 secondsinto recording, subject magnetization has approached steady state.

[0378] A section from this %BOLD difference activation volume may bedisplayed to the subject and the device operator that includes the areaselected as the region of interest as described in section 4 above. Anexample of how this might be presented is shown in 9950. Viewing thisactivation map may allow the device operator to continuously assess theactivity in the brain region of interest during training, andpotentially to stop training, relay information to the subject, orchange the selected region of interest.

[0379] iii. Displaying Activity Metrics

[0380] From the %BOLD difference activation map, activity metrics may becomputed corresponding to the physiological activity in a region ofinterest. A first activity metric may be the average activity in theselected region of interest, for example an area including the primarymotor cortex. This activity metric may also be displayed to the subjectand the device operator, for example as shown in FIG. 5, ROI activity9600. This display may take the form of a scrolling line chart. Thisprovides nearly-instant information to the subject regarding theactivity level metric achieved in the region of interest.

[0381] Viewing this chart may allow the subject to make ongoingassessments of the level of activation of the selected region ofinterest. These assessments of the level of activation may aid thesubject in better performance of the task that they are undertaking toactivate the brain region depicted, or in better performance ofconcurrent behavioral trials such as making a sensory discrimination.These assessments of the level of activation may aid the subject indetermining which strategies for producing brain activation patterns aremost effective, or in selecting which strategies to employ in thefuture. These assessments of the level of activation may aid the subjectin learning how to best activate a localized brain region. Theseassessments of the level of activation may also aid the device operatorin controlling the progress of training. These assessments of the levelof activation may aid the device operator in determining whether to endtraining, in determining which stimuli or behaviors to employ, or inproviding instructions to the subject.

[0382] Activity metrics may also be measured for comparing regions ofinterest, such as regions that are not undergoing training. It may beuseful to measure activity metrics for comparison regions of interest toserve as a negative control for the primary region of interest,indicating that training has a selective effect on the primary region ofinterest rather than on broader areas of the brain. This information mayalso be presented to the subject or device operator as shown in examplepanel 9700. The activity seen in these metrics are frequently anindication of the overall arousal state of the subject. Usinginformation from these metrics may help the subject to gain greaterselectivity in controlling the region undergoing the training processrather than other regions. Information is also computed about thedifference in activation between the primary region of interest and asecondary region of interest, which provides a selective measure of theincrease in activity pattern within the region of interest less anyoverall changes affecting the brain more broadly.

[0383] iv. Displaying Movement Metrics

[0384] Another type of metric typically computed during training may bea set of movement metrics. The data collected may be used to deriveinformation on the position of the subject within the scanner, and thisin turn may be used to determine an ongoing measure of the subjectstranslational movement in 3-D, as well as roll, pitch, and yaw. Thisinformation may be provided to the subject to help them in maintaining astationary position within the scanner, as for example shown in 11000.If movement parameters deviate outside define limits, the subject may beprovided with warnings to maintain stillness within the scanner.Movement metrics may also be provided to the device operator to allowthem to assess the movement of the subject, and abort training orprovide information to the subject if movement is excessive. Movementinformation may also be fed into computations that allow forsubstantially real time movement correction of the scan volumescollected. Examples of the computation of movement metrics is describedin Examples section 1.D.v.

[0385] C. Influencing Subject Behavior

[0386] As has been noted previously, a feature of the present inventionis the performance of training exercises where information, stimuli orinstructions for behavior are communicated to the subject throughvisual, auditory or other signaling. Preferably, what information,stimuli or instructions for behavior are used, and when and how theinformation, stimuli or instructions for behavior are used are at leastpartially based upon previously measured activities. In some instances,the previously measured activities may be from immediately precedingmeasured activities. This is made possible by measuring activities insubstantially real time. In other instances, the previously measuredactivities may be activities associated with different earlier stimulior instructions for behavior that were used.

[0387] i. Selecting the Next Stimulus/Behavior

[0388] A stimulus or instruction may be given to a subject representingsomething to perceive, or a suggestion for what the subject should do,such as an instruction to attempt to increase the level of activity in atarget brain region, observe a presented stimulus, or engage in anaction or cognitive activity. It is noted that the analysis and controlsoftware may take as an input previously measured activities and usethat data to control what, when and how information, stimuli orinstructions for behavior are communicated to the subject. The softwaremay select what stimulus or behavior the subject will be engaged withfor a trial. When the subject begins to perceive this stimulus, orengage in this behavior, this will cause a set of related changes in thebrain of the subject. These changes may also be measured. In some cases,the subject may provide an overt response to the selected stimuli orinstructions as well, as would be the case if the subject werecompleting a sensory discrimination task.

[0389] The stimulus or behavior used in a trial may be selected from theeffective stimuli or instructions for behavior set. This selection maybe a random selection from the effective stimuli or instructions forbehavior set, may be based upon the measured activities of one or morepreceding trials, may be selected based upon behavioral performance, ormay be guided by the subject themselves or by the device operator. Forthe purpose of training a subject, the object of a trial may typicallybe to maximally activate one or more discretely localized brain regions.In such instances, selection of the stimulus/behavior to be used for thenext trial may be based on measured information such thatstimulus/behavior is able to effectively activate the one or morediscretely localized brain regions being trained, or to help the subjectto activation those discretely localized brain regions. If theactivation created by different stimuli or instructions for behavior hasbeen measured, then stimuli can be selected that lead to the greatestactivation level. This can be useful for driving an increase inactivation level when the object of training is to increase theactivation of a target brain region, as might be the case for acondition involving a deficiency in this brain region.

[0390] As an example of stimulus selection, if there are 5 stimuli tochoose between in the effective set, the software may compute an averageof the %BOLD difference measured during presentation of each of thesefive stimuli. The software may then select for the next trainingstimulus the stimulus with the highest %BOLD difference, in order todrive a high level of activation. Alternatively, the software may selectthe stimulus with the lowest %BOLD difference in order to instruct thesubject to increase his or her ability to drive a larger % BOLDdifference for that stimulus.

[0391] As another example, the software may use adaptive tracking byselecting stimuli that drive lower activity when the subject has hadsome number of high activity trials, and stimuli that drive higheractivity when the subject has had some number of low activity trials.

[0392] As another example, stimuli can be selected that drive thehighest levels of a pattern of activity as determined by a patternmetric in the region of interest (see examples 1.D.). This can be usedin cases where such a pattern is the target of training, as might be thecase for a condition involving a two brain regions where a deficiency inactivity in one area leads to a hyper-activity in a second area that thefirst area normally regulates or inhibits. In this case, stimuli mightbe selected that tend to lead the subject to activate the area with thedeficiency, while inactivating the hyper-active area. A number of otherexample methods for triggering the timing and selection of stimuli orinstructions for behavior is described below in section 1 and 3 of theExamples.

[0393] ii. Selecting when to Initiate a Trial or Part of a Trial

[0394] It is often desirable for a subject to begin a particular trialor part of a trial at a moment that is determined based upon themeasured physiological activity up to that point, such receiving astimulus or engaging in a particular action or training exercise when anactivation metric reaches a threshold level. The dataanalysis/behavioral control software 130 can function to select timepoints for initiation of a trial when a particular activity metric is ata determined high or low value, or crosses a threshold value. Subjectscan perform tasks more effectively, learn and remember more effectively,and undergo more effective and more rapid learning and training whentrials are begun at times when the observed value of the activity metricfor a relevant region of interest is above a threshold value.

[0395] One example of identifying when to begin a trial is beginning atrial when an activity metric measured from a region of interest hasreached a criterion level, such as a criterion activation level. If, forthe purpose of training it is desirable for a subject to achieve highlevels of activation in a particular region of interest, then trainingtrials can be begun at time points when the activation level for thatregion of interest is already above a defined threshold level. In thisway, all trials are guaranteed to begin at times when the activity levelis in a target zone, and the subject is trained to maintain the activityat this high level.

[0396] A simple example of selecting when to initiate trials uses afixed trial duration. In this instance, it is sufficient for training tobegin trials on a regular interval, for example each 60 second trialbeginning at the end of the preceding trial, and begin the trainingportion of the trial at a fixed time, for example after a 30 second restperiod. Further examples of selecting when to initiate a trial arepresented in Examples section 3.

[0397] iii. Displaying an Instruction to a Subject

[0398] When the time has been selected as just described, an instructionmay be presented to the subject using a display such as that shown inFIG. 5, or other display elements as described in section 3 or in theexamples. The instruction may be to engage in a period of exercise byobserving a presented stimulus or to engage in a behavior or action. Aninstruction may represent an instruction for what the subject should do,such as attempting to increase the level of activity in a target brainregion, observing a presented stimulus, or engaging in an action orcognitive activity. For example, the subject may receive the textinstruction “activate the region of interest above the performancetarget beginning now, observing the presented stimulus.” In some cases,the task may require the subject to provide a response, as would be thecase if the subject were completing a sensory discrimination task.

[0399] iv. Displaying Stimulus to Subject

[0400] A stimulus may be presented to the subject for the subject toexperience. The timing of presentation and content of the stimulus givenmay be based upon a preceding activity metric measured from the subjectin substantially real time, as has just been described. Visual stimulimay be presented on one of the display panels viewed by the subject orthe device operator, for example as described in FIG. 5, or otherdisplay elements as described in section 3 or in the examples. Forexample, the subject may be presented with a visual image of a body partthat the subject should imagine moving. When the subject begins toexperience the stimulus this leads to changes in the brain of thesubject resulting from sensory stimulation and cognitive processing.These changes may also be measured. Stimuli may also be presented tosubjects using additional stimulation devices providing for stimulationother than visual stimulation, such as using auditory, tactile,proprioceptive, odorant, temperature, gustatory or other stimuli.

[0401] D. Analysis of Subject's Activation Performance

[0402] Once a trial has been performed and one or more activity metricshave been computed for a region of interest, the subject's performanceat modulating the activity metric(s) can be assessed, and the subjectand device operator can be provided with the resulting information. Anumber of measures can be computed of the subject's performance. Thesein turn can be used to set performance targets.

[0403] i. Activation Performance for a Trial

[0404] The subject's activation performance may be monitored throughouteach trial, and the resultant information may be presented to thesubject and to the device operator both during the trial and at the endof the trial. The activation performance that is monitored may includeone or more activity metric being measured from a region of interest.This activation performance may also be a comparison of the activitymetric with a performance target set for the subject. These may bepresented on one of the display panels viewed by the subject or thedevice operator, for example as described in FIG. 5, or other displayelements as described in section 3 or in the examples, such as an ROIactivity panel 11600 with a corresponding performance target 11640indicating the level that the subject is supposed to reach.

[0405] Typically activation performance may compare an activity levelmetric between a rest period and an exercise period of a trial such asthe period when the subject is engaging in a task, perceiving astimulus, or attempting to modulate the level of an activity metric. Onetype of activation performance measure may be the difference between theaverage of the activity metric during the stimulus/behavior period andduring the background period. Another type of activation performancemeasure may be the average of the activity metric during thestimulus/behavior period alone. Another type of activation performancemeasure may be the average of the activity metric during the backgroundperiod alone. Another type of activation performance measure may be ameasure of whether the average of the activity metric during thestimulus/behavior period was above a performance target set for thesubject. Another type of activation performance measure may be thepercentage of the stimulus/behavior period during which the activitymetric was above the performance target set for the subject. Anothertype of activation performance measure may be the amount by which theactivity metric was above the performance target set for the subject.These types of information can all be presented to the subject or deviceoperator to allow ongoing information about the subject's performance onthe most recent trial or over a number of trials. This information maybe presented, for example, using display panels 11300 and 11600. This isuseful in aiding the subject's motivation, in helping to selectstrategies, and is helpful to training.

[0406] Once the activation performance has been measured, it is possibleto designate whether a trial has been successful based upon theactivation performance. Correct or successful trials may be defined astrials when a subject maintained an activation performance level onaverage above a performance target for the period of activation,stimulus, or behavior.

[0407] Based upon the subject's achieved activity level metric on thetrial relative to the target level, the subject can be given rewards fortheir positive performance, or punishment for poor performance. It maybe sufficient reward or negative reinforcement to indicate to thesubject whether they have succeeded and give them a ‘score’ based upontheir achieved level of activation and number of successful trials.Subjects can also be given additional rewards to achieve bettermotivation as described in the examples section. The subject can also begiven additional information, instructions, or suggestions to try toimprove their performance on future trials. This can come straight fromthe device operator who may provide this information, or it may begenerated by the analysis and control software. These may be presentedon text instruction panels such as shown in 10900. Exampleinformation/suggestions that can be derived from the observed patternsof activity:

[0408] Activity metric for the preceding trial was high in the stimulusperiod relative to the background: 1) “Great job, keep up the good workand use similar strategies”. Activity metric for the preceding trial waslow in the stimulus period relative to the background: 2) “That trialwas less successful, perhaps you can try a different strategy orincrease effort”. Movement metric for the preceding trial was high: 3)“Try to remain as motionless as possible within the scanner”. Activitymetric for the preceding trial rose slowing or late following aninstruction to initiate activation: 4) “Try to time your activationpattern so that it starts promptly at the beginning of the trial”.Activity metric for the preceding trial fell before the prescribedactivation period had ended: 5) “Try to maintain your activationthroughout the length of the trial”.

[0409] ii. Activation Performance for Multiple Trials

[0410] Once activation performance and trial success computations havebeen computed for individual trials, they then may be combined toanalyze the subject's performance across trials. For instance, thepercent of successful trials may be computed, using the percent oftrials when the subject maintained the activity metric above theperformance target on average during the stimulus/behavior period. Thepercent of correct trials may be computed and displayed for differenttrial types or periods of time, for example as shown in 11500.

[0411] The level of difference in activation between thestimulus/behavior condition and the background condition may also becomputed and displayed for different trial types or periods of time, forexample as shown in 12050.

[0412] iii. Setting Performance Targets

[0413] Activation performance results and success results may be used tocompute performance targets which may be displayed to the subject. Aperformance target may be set initially, and continually adjustedthroughout training in order to ensure that training is constantlychallenging, but achievable for the subject. This performance target maybe presented to the subject before or during each trial as an indicationof the level of an activity metric that the subject is intended toachieve. For example, when the subject views a graph of the on-goinglevel of activation in a region of interest, a bar may be displayed onthe chart indicating the level of the activity metric that the subjectis intended to achieve during the stimulus or behavior periods of thetrial. This is particularly effective when high-pass filtering is usedin the activity metric to remove baseline drift. This target performancelevel constitutes an instruction to the subject to achieve a certainperformance level during the trial.

[0414] One method of setting and continuously adjusting performancetargets is to use adaptive tracking. In this methodology, an initialperformance target may be set to a value that it is anticipated that thesubject will be able to achieve, such as one standard deviation abovethe mean of an activity metric. Using adaptive tracking the performancetarget may be made more challenging when the subject achieves somenumber of successful trials in a row, such as three. The performancetarget may be made less challenging when the subject fails to achievesuccess on some number of trials in a row, such as one. Other methods ofadaptive tracking are familiar to one skilled in the art. When theperformance target is made more challenging, the subject can be alertedthat they have moved up to a more challenging level, and when it is madeeasier they can be alerted that they have been moved down to a lesschallenging level. The subject's goal, of course, is to achieve thehigher levels. The performance target may be increased or decreased by afixed amount, such as one half of its current value, or by an amountbased upon the activity metric, such as some fraction of a standarddeviation of the activity metric.

[0415] Before or during each trial, the subject may be presented with atarget level of the activity level metric that they are intended toreach or exceed on average throughout the trial. In one example, thisperformance target is presented on an ROI activity metric chart 11600 atthe time that the subject is supposed to exceed this performance targetlevel. Following the trial, the measured activity level metric iscompared with the target to determine whether the subject succeeded inachieving the target activity level metric during the trialstimulus/behavior period. The change in the activity level metric fromprimary region of interest minus the change in an activity level metricfrom comparison regions of interest are also computed and presented tothe subject and device operator. Performance target tracking informationand the current difficulty level may be conveyed to the subject eitheras text, via digitized speech, or through a graphical representationsuch as a performance target line on the user interface indicating thetarget level of the activity metric.

[0416] E. Analysis of Subject's Behavioral Performance

[0417] If subjects are performing a behavioral task and therefore makingovert behavioral responses during the trial period, then theirperformance at this task is analyzed to assess their behavioralperformance. For instance, if a subjects is performing a visual stimulusdiscrimination task designed to activate visual sensory areas duringtraining, then performance on this task may be computed for each trial.For each trial, the subject provides a response (e.g. a button-pressindicating which of two alternative areas contained a visual stimulus).This response may be selected on a panel similar to 13500. The analysisand control software records these responses and makes computations ofthe subjects performance level. These computations correspond totypically measured psychophyisical parameters (see Green, D. M. andSwets, J. A. Signal detection theory and psychophysics. New York: Wiley,1966). For instance, if sensory discrimination is being made on a numberof stimuli along a continuum from easy to hard, the percent correct foreach stimulus type is computed in order to generate a performance curveand determine a 50% correct threshold. Percent correct measures may bemade in the same fashion for motor or cognitive tasks. These allow thecomputation of psychophysical parameters such as d′ and beta accordingto standard methods familiar to one skilled in the art. The subject maybe informed on each trial whether their response was correct orincorrect.

[0418] In one example, subjects may be trained to assess the level of anactivity metric, such as the level of activation of a particular brainregion, without being able to see information about that metric. In thisinstance, subjects may be cued to respond with an estimate of theactivity metric at a given time, and may then present that response. Forexample, they may respond that the metric is either high or low, or theymay make an estimate on a scale. In this case, their behavioralperformance may be presented to them as an indication of how accuratetheir estimate was. This is useful in training subjects to be able toassess the level of physiological activity in a localized brain regionof interest in the absence of externally provided information about thislevel.

[0419] F. Repeating Trials and Training Blocks

[0420] Behavioral trials as described thus far in section 6 may berepeated throughout a training block, typically lasting 10-30 minuteswith substantially continuous physiological measurement throughout.Training blocks then may be repeated as well, with 1-10 training blockstaking place in one training session, and multiple training sessionstaking place on the same day or different days.

[0421] G. Blind Trials

[0422] In some trials the subject may not be provided with informationabout their brain activity, or may be provided with sham informationbased on random fluctuations or information from a different brainregion of interest or a previous time. These trials allow an estimate ofthe performance that the subject can achieve without the presence of thescanning information, or in the presence of false or random information.

[0423] H. Recording Progress of Exercise and Treatment

[0424] The subject's progress over each training session is monitored,and subjects and device operators are provided with information of theprogress. A principle type of information may be the average level ofthe activity metric for the region of interest that the subject was ableto achieve during each training trial, training block, and trainingsession.

[0425] It should not be lost that training may be directed towardimproving a particular condition that is to be treated. Accordingly, itis important that the progress of the subject also be measured in termsof signs and symptoms of the condition being treated, as well asbehavioral performance. This information may also be presented to thesubject and device operator.

[0426] I. Subject's Decreasing Need for Measurement Information

[0427] In general, the changes in brain activation that subjects aretrained on through the use of this invention may be enduring outside ofthe context of brain physiology measurement. Increases in the strengthof activation of neural areas can be thought of as being analogous tothe increase in muscle strength achieve through weight lifting, whichpersists outside of the context of the weight-training facility. It isdesirable for subjects to be able to modulate brain activation in theabsence of a measurement device, and this process of transfer of brainactivation patterns to contexts outside of the measurement of brainphysiology can be facilitated. Subjects may be ‘weaned’ from the needfor information about activity metrics to successfully modulate brainregions. This may take place by continuing to measure the subject'slevel of activity, but increasing the duration of time when the subjectis not given access to information about the indicator during trials.Eventually, the subject may come to be able to control the physiologicalstate without access to the indicator at all. It may also be possible tocontinue to give access to the indicator, but with increasinglydiminishing levels of information being present in the indicator. Forexample, the indicator can increasingly be diminished in amplitude untilit is difficult to assess its value. Ultimately, it may be possiblethrough training with spatially-localized physiological indicators toteach subjects to control spatially-localized patterns of physiologicalactivity even in the absence of the indicators that were initially usedin training.

[0428] J. Performing Training Exercises in the Absence of Scanning

[0429] An aspect of this invention relates to a subject performingtraining that is effective in regulating physiological activity in oneor more regions of interest of that subject's brain in the absence ofinformation regarding the subject's brain states. Once optimal stimulihave been selected using physiological measurement, and/or a subject hasbeen trained in controlling an activity metric in a region of interestwith the presence of information about this activity metric, thesubjects may be trained to continue to achieve this control and exerciseof the corresponding brain regions in the absence of substantially realtime information regarding the activity metric. This training can takeplace using training software largely analogous to that used inside thetraining apparatus, but run on a different computer. This computer doesnot have to be connected to physiological measurement apparatus. Inplace of real brain measurement information, the software can either usesimulated information, such as random information, or it can useinformation from the same subject collected during scanning, or it canuse no information at all and omit presentation of activity metrics.

[0430] In this method, stimuli or instructions for behaviors areselected based upon their observed ability to modulate a measuredactivity metric. This selection of stimuli is described in Examplessection 3. Stimuli used may also have been derived as described insection 3, omitting the process described in section 5. For example, asubject may be trained at the modulation of a region including the motorcortex. The subject may use imagined movements as a behavior. Theimagined movements that lead to the greatest pattern of activation maybe determined by having the subject imagine those movements and othermovements, and determine which ones lead to the highest level ofactivation in the region of interest. Then, in the absence of themeasurement apparatus, the subject may use software that instructs thesubject to engage in training using the same selected set of behaviors.This software can be the same software that the subject used while inthe measurement apparatus, or different software. These stimuli thathave been demonstrated to be effective can be used for the training ofother subjects to activate similar brain regions.

[0431] K. Prescribing Ongoing or Follow-On Treatments as Needed

[0432] The exercise described in this invention can be combined withadditional forms of therapy, such as pharmaceutical or rehabilitativemedicine treatment. Accordingly, a medical professional monitoring theprogress of the subject in regard to the subject's condition mayprescribe additional training, change the training schedule, ordiscontinuing training as the need arises. The medical professional mayalso wish to prescribe or recommend training outside of the scannerusing training simulation software. In other cases, the subject may berequired to undergo follow-up in the scanner training or otheractivities and check ups periodically following initial training.

EXAMPLES

[0433] The brain is highly segmented, with localized regions of thebrain performing entirely different functions. Hence, in order to havean impact upon a given brain disorder, it is necessary to be able toregulate a specific region of the brain. As described above, the presentinvention allows a subject to first identify what training exercises areeffective for that subject in order to regulate a given region ofinterest, and then allows the subject train and exercise the region, andto evaluate how effectively the subject is applying the trainingexercise in substantially real time so that more effective applicationof the exercises can be achieved by the subject. Now that such selectiveactivation of regions of interest of the brain can be achieved, a myriadof valuable applications are made possible. Described herein is anon-comprehensive list of different applications of the methods of thepresent invention. Also described are more detailed examples of thetypes of information that may be provided to subjects and of the typesof computations used to generate these displays.

[0434] 1. Performing Computations on Images Using Analysis and ControlSoftware

[0435] The data analysis/behavioral control software 130 may be used totake in raw image data and perform a series of computations, includingpre-processing 135, computation of activation image/volumes 137,computation of activity metrics 140, and selection, generation andtriggering of information such as measured information, stimuli, orinstructions 150. A single example of these steps were presented insections 3-6 above. The following sections provide more detailedexamples and explanations. The results of the computations describedhere are presented to the subject of the experiment or used to controlits progress. It is noted that the examples provided herein relate tofMRI data processing. However, analogous methods may also be developedfor other types of physiological data. The examples presented here canbe performed using the functions developed in Matlab version 6.1provided by the Mathworks, Inc., and its associated toolboxes such asthe statistics, image processing, and digital signal processingtoolboxes.

[0436] A. Data Pre-Processing

[0437] Physiological data received by the analysis and control softwareare in the form of raw T2* weighted 2-D or 3-D scan images/volumes 125.These data can be pre-processed using a variety of methods. One type ofpre-processing that may be performed on the input image/volume data maybe to pass the input image/volume data as output through to the nextstep of computing activation images/volumes without any furtherpre-processing. The resultant output is a set of 2-D or 3-D scanimages/volumes that have undergone computations as described. Each ofthe methods described in this section can take the raw image/volume data125 as its input, or can take the output of one of the other methodsdescribed in this section as its input. Further detail on each of thesemethods is provided in user manuals for Matlab ver 6.1, as well as inthe user manuals and documentation for existing MRI/fMRI/PET dataprocessing packages.

[0438] Spatial Smoothing

[0439] One type of pre-processing that may be performed on the inputimage/volume data may be spatial smoothing according to standard methodsto produce smoothed image/volume output data. This is useful because itremoves noise in the data, improves statistical properties by making thedata variance more gaussian, and produces an image that is easier tointerpret visually. This is accomplished by convolving the data with a2-D or 3-D gaussian filter function with a defined half-width.

[0440] ii. Temporal Filtering

[0441] Another type of pre-processing that may be performed on the inputimage/volume data may be temporal filtering including lowpass, highpass,bandpass filtering and convolving with a function such as a hemodynamicresponse function. This is useful because it removes temporal noise inthe data, matches the signal power in the data to that corresponding tothe trials being conducted, and improves later data processing andstatistical measures. This is accomplished by convolving the data with atemporal filter. This convolution will normally be with a causal filteras the data is being collected in substantially real time. The filtercan be a highpass filter, such as a highpass filter with the cutoff of10,30,60,120,240,300s, or the lowest relevant frequency component of thebehavioral trials being conducted, or a drift rate that reflects theslowest relevant physiological change expected in the signal. The filtercan be a lowpass filter, such as a lowpass filter or gaussian functionwith the cutoff of 0.25, 0.5, 1, 2, 4, 5, 10 s. The filter can be alowpass filter designed to match the shape of a hemodynamic responsefunction modeled as an alpha function. The filter can be a bandpassfilter that accommodates a combination of highpass and lowpasscharacteristics. These filters can be designed using standard digitalfilter design techniques.

[0442] iii. Slice Time Correction

[0443] Another type of pre-processing that may be performed on the inputimage/volume data may be slice time correction to correct for the timeof collection of each slice by interpolation. This is useful because itapproximates the case where each slice in a scan volume was collectedsimultaneously. In order to perform this computation, the relative timesof collection for each slice in a scan volume are known. The first imagein each volume is taken as the reference image. The output values foreach successive image in the volume are computed as the interpolatedvalue between the measured value for each voxel in the image and themeasured value for the same voxel in the previous image or succeeding.The interpolation yields the value corresponding to the estimated valuefor the voxel at the time point actually measured for the referenceimage. This standard method is described in the literature and inmanuals for existing MRI/fMRI/PET data processing packages.

[0444] iv. Transformation into Standard Coordinates

[0445] Another type of pre-processing that may be performed on the inputimage/volume data may be a transformation into standard coordinates byapplying a transformation vector that yields the corresponding value ateach voxel in a standard coordinate space. This matrix is predeterminedas described in Examples section 6. This has the advantage that allsubsequent processing and display of data is in a standard coordinatespace such as Talairach space or MNI space that can be directly comparedwith reference data.

[0446] V. Resampling of Data

[0447] Another type of pre-processing that may be performed on the inputimage/volume data may be resampling to increase or decrease the temporaland spatial resolution of the data, using band-limited filtering ifneeded. Resampling can produce a more detailed or less detailed view ofthe collected data. It can also be used to match the sampling of thedata to that used in data set to which it will be compared, such asanatomical data collected for the subject, or data from a standardsubject. Resampling can be performed using standard methods.

[0448] vi. Motion Correction of Data

[0449] Another type of pre-processing that may be performed on the inputimage/volume data may be motion correction to adjust for the motion thattakes place between subsequent scans. This is useful because eachsection of each volume is in substantially the same position as in thefirst or reference scan of a scanning session. This can take place byapplying using a transform created for each scan volume to that scanvolume. The transform is designed to create the best fi it in theleast-squared error sense between the data of the current scan and thereference scan, including translation, rotation, and scaling if needed.An example of this software is described in: CC Lee, et al. Real-timeadaptive motion correction in functional MRI. Magn Reson Med1996;36:536-444 and in manuals and literature associated with existingMRI/fMRI/PET data processing packages. Each of these steps, which cantake place individually or in combination and in any order, will befamiliar to one skilled in the art. These pre-processing steps may beapplied to one or more reference scan, typically an early scan from thescanning session that will be used as a basis of comparison forcomputing activation images/volumes. These preprocessing steps may alsobe applied to each successive scan collected. The pre-processing for thereference scan(s) need not be the same as for subsequent scans. Thesepre-processing steps lead to pre-processed scan volumes for each sampledtime point, which are then used for further computation and processing.The use of motion correction software may be used to allow motion of thesubject relative to the measurement apparatus while measurements arecollected and/or training is conducted, those measurements beingcorrected so that voxels correspond to the appropriate locations withinthe brain of the subject.

[0450] vi. Regression Filtering

[0451] Another type of pre-processing that may be performed on the inputimage/volume data may be regression filtering to remove noise componentsassociated with exogenous events. For example, the activity level ineach voxel may be correlated with an event not directly related totraining, such as the phase of the cardiac or respiratory cycle. Thedata from each voxel may be corrected by regressing out this noisesource. This method is described in the literature, for example in J. T.Voyvodic, NeuroImage 10, 91-106 (1999).

[0452] vii. Selection of Voxels Corresponding to Brain

[0453] Another type of pre-processing that may be performed on the inputimage/volume data may be the selection of voxels corresponding to thebrain. This process may include the masking off of voxels determined tobe outside of the region corresponding to the brain, such as voxelscorresponding to the skull and regions outside of the head. This processmay also include the masking on of voxels determined to be inside theregion corresponding to the brain. This process may take placeautomatically under software control. Algorithms for this process aredescribed in the literature and is known to one skilled in the art.

[0454] B. Computation of Activation Images/Volumes

[0455] Activation image/volumes may be computed taking as input a set ofthe pre-processed scan images/volumes, normally the entire set generatedsince a scanning session began. The activation image/volumes that aregenerated as output indicate the level of physiological activation ateach voxel on the map. These maps may represent various measures of thesecond-by-second blood oxygenation level in the subject's brain regionsthat is an indicator of blood flow, and of brain metabolism and neuralactivation. These activation images/volumes, in turn, may be used asinput to generate additional activation images/volumes, or to computeactivity metrics from localized brain regions. These activationimages/volumes may also be used as inputs to the displays that will bepresented to the subject or the device operator.

[0456] i. Raw T2* Weighted MRI Signal

[0457] One type of activation image/volume that may be computed is theraw T2* weighted MRI is. This is the pre-processed output from theprevious step. In this case, no further processing is performed at thisstep. This is useful primarily as a display of the raw signal, forexample to appreciate any potential problems with data acquisition.

[0458] ii. Difference Images Including BOLD Difference Images/Volumes

[0459] Another type of activation image/volume that may be computed isthe difference image, including BOLD difference images. One primary typeof difference image is the measured difference in level between two timepoints. A single T2* weighted image by itself gives little informationabout the activity level at each voxel position, because the valuesmeasured primarily reflect the anatomical composition of the underlyingtissue with a small contribution (e.g. 1%) from the physiologicalsignal. By comparing images measured during different conditions, theanatomical portion of the signal will be essentially unchanged, but theportion of the signal corresponding to the physiological activation willbe different. This is useful because it provides a measure of the changein physiological activation between two time points. Thus, thedifference in T2* signal intensity between two time points is anindicator of the difference in physiological activation between thosetwo time points. There are a variety of choices of what difference tocompute, for example how many time points to average over beforecomputing a difference.

[0460] Normally, a reference scan image or volume may be selected, whichmay then be subtracted from subsequent images or volumes. This referencevolume can be the first scan of a session, or one of the early scans ofa session because the first scan may be unrepresentative due to tissuemagnetization not having reached steady-state.

[0461] One difference image/volume can be computed by subtracting thevalue at each voxel in the reference scan from the value in thecurrently measured scan. Another difference image/volume can be computedby subtracting the average value over a defined time period before thecurrent scan from the value in the currently measured scan, useful ifthe steady-state level measured is drifting over time. Anotherdifference image/volume can be computed by subtracting the time-filteredand/or spatially smoothed value from a time period before the currentscan from the value of the currently measured scan, also useful toreduce noise and correct for baseline drift. Another differenceimage/volume can be computed by subtracting the average value from aseries of reference scans collected during one or more background orrest conditions, useful when an average background level is the mostappropriate for taking a difference. Another difference image/volume canbe computed by subtracting the average value from a series of referencescans collected during one or more behavior or stimulus conditions,useful when an average activated level is the most appropriate fortaking a difference.

[0462] iii. % Difference Images/Volumes

[0463] Another type of activation image/volume that may be computed isthe percent difference image/volume, computed by normalizing themeasured difference image/volume in order to produce an image/volume inunits of fractional difference, or percent difference. For example, a%BOLD difference image/volume is computed by taking a single differenceimage/volume and dividing it by a reference image/volume. At each voxel,the resultant %BOLD signal equals, for example 100%×(signal at timepoint−signal at reference time point)/(signal at reference time point).% difference signal images/volumes can be computed by taking any of theabove difference signal images/volumes, and dividing them by theircorresponding reference or average reference images/volumes.

[0464] iv. Variance Images/Volumes

[0465] Another type of activation image/volume that may be computed is avariance image/volume. The variance of any pixel or group of pixels overa period of time can be computed, and these values can be formed into avariance image/volume. These images can be useful in located bloodvessels, which might be excluded from further analysis in certaininstances where brain matter physiology is the target, or focused uponif vascular perfusion is the target.

[0466] v. Statistical Contrast Images/Volumes

[0467] Another type of activation image/volume that may be computed is astatistical contrast image/volume. Images and volumes can also becomputed based upon statistical measures of activation for each voxel.This may be useful because these maps indicate measures of thereliability with which a given voxel's activity correlates with somecondition(s), such as a stimulus, or behavior. One type of statisticalcontrast map that can be computed may be a t-test map, that may computethe p-value from a t-test comparing the set of measurements for a voxelduring one condition, such as a background or rest condition, with themeasurements during a different condition, such as a stimulus orbehavior condition. Another type of statistical contrast map may be anF-test map, that may make a comparison of these same sets ofmeasurements using an F-test and a predictor model such as a boxcar orsin-wave function representing different behavioral periods, or a boxcarfunction convolved with a haemodynamic response function such as analpha function. Another type of statistical contrast map is a map thatmay be corrected for the large number of degrees of freedom inherent infMRI data reflecting serial measurements, or corrected for spatialcorrelation among proximate voxels. The computations involved have beendescribed extensively in the literature, and in the manuals andsupporting literature for existing MRI/fMRI/PET data processingpackages.

[0468] vi. Contour Maps of Activation Images/Volumes

[0469] Another type of activation image/volume that may be computed is acontour map, which may be computed to designate the contour lines on anactivation image or volume for a set of one or more contrast levels.This may be useful for displaying and viewing activation images/volumes,or for localizing regions of activation.

[0470] vii. Thresholded Maps of Activation Images/Volumes

[0471] Another type of activation image/volume that may be computed is athresholded map. Thresholds may be computed and used to cut out certainmost relevant portions of the data from activation images/maps.Thresholds can be defined as a mean value of a region, or some fractionof the mean value. The fraction can be defined by a measure of thevariance. An example threshold would be two standard deviations belowthe mean value of an entire activity pattern image. In some cases it maybe helpful to set all values below or above a set threshold to abackground level.

[0472] C. Displaying Activation Images/Volumes

[0473] Anatomical and physiological data representations may bepresented to the subject in substantially real time using a display 180.In addition, these data may be presented to a device operator on one ormore additional displays. In one embodiment, activation image/volumedata from an fMRI is transformed into a variety of intensity-coded orcolor-coded 2-D image maps. These maps may be presented a 2-D sections,such as coronal, sagittal, axial, or oblique sections. They may also bepresented as 3-D images such as transpart or cutaway volume images,rendered 3-D volume images, or wire-mesh images. Physiologicalmeasurements can also be overlayed onto anatomical measurements eitherusing 2-D anatomical images as seen in 9950 or 3-D rendered brainimages. These methods are familiar to one skilled in the art and aredescribed in available documentation for existing MRI/fMRI/PET dataprocessing packages (see definitions). The resultant images arepresented using the displays described in Examples section 2.

[0474] D. Computation of Activity Metrics

[0475] Data from activation images/volumes can be used to computeactivity metrics. These activity metrics are computed measures fromregions of interest within activation images/volumes. The input to thesecomputations are the time series data from a single measurement point orvoxel, or from a group of voxels that constitute a region of interest oran entire image or volume. A simple example of an activity metric is anaverage value at a single time point for all of the voxels within aregion of interest. Some example activity metrics are described here.All of these metrics can be computed in substantially real time.

[0476] i. Average Value Metrics at a Single Time Point

[0477] One type of activity metric that may be computed is the averagevalue from a region of interest at a single time point. This value givesan indication of the average level of activation for the region ofinterest, which can be used in training subjects to increase or decreasethis level of activation.

[0478] ii. Spatial Pattern Comparison Metrics

[0479] Another type of activity metric that may be computed is a spatialpattern comparison metric. Spatial pattern comparison metrics can beused to compare the pattern of activity in a region of interest with atarget or reference pattern. This is useful, for instance, if a subjectis being trained to approximate a target pattern of activation. In thiscase, the subject receives information regarding the difference betweenthe currently measured pattern and the target pattern, and is trained todecrease this difference. One type of spatial pattern comparison metriccan be computed as the sum of the voxel-by-voxel differences between thecurrent pattern and the target pattern in an ROI, indicating overallcloseness to the target. Another type of spatial pattern comparisonmetric can be computed as the sum of the voxel-by-voxel sums of thecurrent pattern and the target pattern in an ROI. The two precedingspatial pattern comparison metrics can be divided by the target patternsum to give a percentage value. Another type of spatial patterncomparison metric can be computed as the dot product between the vectorcomprising the current pattern and the vector comprising the targetpattern in an ROI, indicating overall closeness to the target.

[0480] iii. Correlation Metrics

[0481] Another type of activity metric that may be computed is acorrelation metric. Correlation metrics can be computed that correspondto the correlation between the activity of two voxels, or two regions ofinterest over time. This may be useful in training subjects to generategreat correlation between to brain regions, for instance in order tocreate stronger functional coupling between the activity in two brainregions. One type of correlation metric can be computed as a correlationcoefficient between two activity metrics, r. Another type of correlationmetric can be computed as an activity-triggered average between twoactivity metrics, such as the average level of activity at one point forone or more ranges of activity level at another point. Another type ofcorrelation metric can be computed using ‘network analysis’ to determinefunctional connectivity between different points within the brain asdescribed in “Functional neuroimaging: network analysis”, L Nyberg andA.R. McIntosh, in HandBook of Functional Neuroimaging of Cognition edsRoberto Cabeza and Alan Kingstone.

[0482] iv. Threshold Crossing Metrics

[0483] Another type of activity metric that may be computed is athreshold crossing metric. Threshold crossing information can be used tomeasure when an already-computed activity metric crosses a giventhreshold level. This can be useful to indicate to a subject when theyhave achieved a target level of a given activity metric, such as playinga sound that indicates success at those times. Another type of thresholdcrossing metric can be computed as the time when the signal crosses adefined threshold value. Another type of threshold crossing metric canbe computed as an indicator of whether the signal is above or below thatthreshold value. Another type of threshold crossing metric can becomputed as an indicator of whether there has been a change in whetherthe signal is above or below that threshold since the last time point,and the direction of the threshold crossing. Another type of thresholdcrossing metric can be computed as a positive value at time points whenthe threshold is crossed, and a zero value at other time points.

[0484] v. Movement Metrics

[0485] Another type of activity metric that may be computed is amovement metric. Movement information can be used to measure determinewhether a subject's movement in the scanner is confounding othermeasurements. Movement measurements give an indication of the positionor change in position of the subject's head, brain or some otheranatomically defined structure within the scanner. One type of movementmetric take the form of x,y,z cartesian coordinate information, as wellas pitch, roll and yaw rotational information. Another type of movementmetric take the form of the chance in x,y,z Cartesian coordinateinformation, as well as pitch, roll and yaw rotational informationbetween two time points. A position metric can be computed bythresholding the brain scan volume data to zero for values below{fraction (1/)}8^(th) of the mean value, and 1 for values above thisthreshold, and then computing the x,y, and z values for the centroid ofthe resultant volume. This centroid vector can be compared with acentroid vector at a reference time such as the first scan to givemeasures of change in position. Subjects can be instructed to remainmore still if movement exceeds certain limits. More detailed methods forcomputing movement metrics will be familiar to one of ordinary skill andare described in available documentation for existing MRI/fMRI/PET dataprocessing packages.

[0486] vi. Movement Correlation Metrics

[0487] Another type of activity metric that may be computed is amovement correlation metric. Once movement metrics and activity metricshave each been computed, then metrics of the correlation between the twocan be derived. These metrics are helpful in determining whether asubject's movement is contributing significantly to the activity metricsthat have been observed. An F-test can be used to compute therelationship between an activity metric and a movement metric. Once arelationship has been determined, the contribution of the movement canbe regressed out of the activity pattern data. This can yield measuresof activity pattern data in the absence of the contribution of movement.

[0488] vii. Signal Processing Metrics

[0489] Another type of activity metric that may be computed is a signalprocessing metric. A number of other mathematical measures can be madeon activity metrics that provide additional useful information tocharacterize these signals, and in turn to control them. Certain ofthese metrics may correspond with particular behavioral or cognitivestates, and thereby be used as a measure of the presence of thosestates, or to train subjects in reproducing those states. For example,active states may have more power at high frequencies of an activationmetric, whereas passive or relaxed states may have less power at thosehigh frequencies. Example signal processing measures include: the powerspectrum of the activity metric, the power of an activity metric withina limited band-pass filter band, and the spectrogram of the activitymetric.

[0490] viii. Activity Position

[0491] Another type of activity metric that may be computed is anactivity position metric, that may compute the position of highestactivity within a region of interest. In this example, the voxel orgroup of voxels showing the highest level of an activity metric aredetermined. This activity position can in turn be used as a method fordecoding what is being represented by mapped neural activity. It haslong been known that activity in many brain areas is ‘mapped’.Activation in different regions corresponds with particular stimulus ormovement features. For this reason, a center of activation at any onepoint on a map can be used to determine the corresponding feature on aknown map as the feature that is being encoded. This may be useful informing an estimate of what is being represented in the brain of thesubject at any point or period in time. This, in turn, can be used toguide training, such as by selecting a next stimulus of a character thatis related to that which is being coded at a particular moment.

[0492] ix. Vector Average Metrics

[0493] Another type of activity metric that may be computed is a vectoraverage metric. Vector average metrics may involve computing an estimateof the decoded object or feature being represented by a given activitypattern. One example of this decoding is the measurement of a vectoraverage of activity. In this example, the measure of an activity metricat each voxel within a region of interest is computed, and is multipliedby a feature vector assigned to that voxel that corresponds to thevoxel's underlying feature selectivity or representational function. Thevectors are then averaged to produce a vector average activity metric.This vector average can be used to compute an estimated feature beingrepresented by the underlying physiology in the region of interest. Thefeature vectors that area used for each voxel may correspond to what thevoxel has been determined to be involved in the processing of, or to thevoxel's relative position on a defined representational map such as acortical map of visual or motor space.

[0494] For example, for visual brain areas, the feature vector for eachvoxel may correspond to a position in visual space, or to a combinationof other visual features, that are represented by activity in the brainof the corresponding voxel. The feature vector may also be determined bya voxel's position on a visuotopic map. For auditory brain areas, thefeature vector for each voxel may by the preferred sound frequency forthat voxel, or to its relative position on a tonotopic map. Forsomatosensory areas, the vectors may be positions on the body that thevoxels are involved in receiving input from, or the voxels relativeposition on a somatotopic map. For motor areas, the feature vectors foreach voxel may be points in space reached by a motion preferentiallyactivating the voxel involved, or may be muscle groups that arepreferentially activated in conjunction with the activation of themeasured voxel. They may also be the information or function designationon a motor map of the area. Taking the motor example, it has been shownthat by taking the vector average of the level of activity times thepreferred movement target for each of a number of points in the motorcortex, an estimate can be made of the movement target for a particularactivation pattern (see Motor area activity during mental rotationstudied by time-resolved single-trial fMRI. W. Richter R. Somorjai R.Summers M. Jarmasz R. S. Menon J. S. Gati A. P. Georgopoulos C. TegelerK. Ugurbil S. G. Kim; J Cogn Neurosci. March, 2000; 12(2):310-20,Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronalpopulation. A. P. Georgopoulos R. E. Kettner A. B. Schwartz J Neurosci.August, 1988; 8(8):2928-37). In this way, the vector average method mayprovide one indication of what is being represented by a given patternof activation within a region of interest.

[0495] x. Feature Decoding Metrics

[0496] Another type of activity metric that may be computed is a featuredecoding metric. Additional methods are available for decoding what isbeing represented by brain areas through computations involving thevector of activity at a large number of points in the brain. Theseadditional decoding metrics may also be useful in forming an estimate ofwhat is being represented in the brain of the subject at any point orperiod in time. This decoding indicates that a relation is formedbetween different states or patterns of activity in a region of interestand objects or movements that may be encoded. Many types of methods havebeen developed for creating this relation (see for instance Real-timecontrol of a robot arm using simultaneously recorded neurons in themotor cortex, J. K. Chapin K. A. Moxon R. S. Markowitz M. A. Nicolelis,Nat Neurosci. July, 1999; 2(7):664-70), and these methods may be used bythis invention. Once an estimate is available of what is beingrepresented in the region of interest, this, in turn, may be used toguide training, such as by selecting a next stimulus of a character thatis related to that which is being represented at a particular moment, ora behavior based upon what is being represented.

[0497] x. Time Average Metrics

[0498] Another type of activity metric that may be computed is a timeaverage metric. Once the activity metrics described have been computed,they can each be averaged over periods of time. Average values can beusefully employed to compare different conditions. In one example of atime average metric, the average of an activation metric can be computedfor all time points within a recent period of time to determine asubject's recent level of activation in an ROI. In another example of atime average metric, the rolling average of an activation metric canalso be computed In another example of a time average metric, averagescan be computed for different types of conditions, such as the averageof a metric for all time points falling within a particular behavioralor stimulation condition. In another example of a time average metric,averages can be computed for all time points falling within a backgroundor rest condition.

[0499] xi. PETH Metrics

[0500] Another type of activity metric that may be computed is aperi-event time histogram metrics (PETH) metric. PETH metrics areparticularly useful for determining the average time course of a metricfollowing a behavioral event, stimulus, or other event PETH metrics arecomputed as the average over several trials of an activity metric,computed separately for a number of time points before or after areference time point, such as the beginning of a trial.

[0501] xii. Likelihood of Behavioral Success Metrics

[0502] Another type of activity metric that may be computed is alikelihood of behavioral success metric. There are some time periodswhen a subject is more likely to succeed at a given task than others. Itis generally desirable to identify when a subject is most likely tosucceed or have a positive outcome in performing a behavioral task suchas a perceptual or behavioral task or training. For example, when theoccipital or temporal cortical brain regions subserving the visualperception of a particular stimulus are activated, and frontal regionsinvolved in extraneous tasks such as unrelated thoughts are notactivation, the subject is more likely to succeed at a visualdiscrimination task. Related findings have also shown that peopleremember better when areas of the brain involved in memory are moreactive. Previous studies have documented this retrospectively.Prospective measures of a subject's activity in a region of interestinvolved in subserving a given task can be used to predict when thesubject will have a positive successful behavior, or perform a taskquickly, or learn or remember more effectively. Therefore, thesemeasures are helpful in training and exercising the subject.

[0503] A measure of the likelihood of success in any task can be madebased upon an activity metric measured before or during a task if thereis some correlation between the activity metric and success in the task.A relationship may be measured between the distribution of activitymetrics over many trials, and the distribution of success at performinga task over many trials. This relationship may include an averagelikelihood of behavioral success for each of a number of ranges of thedistribution of the activity metrics. Using this relationship, it may bepossible to form an estimate of the likelihood of behavioral success fora trial conducted when the activity metric is at any particular value.

[0504] Take for example, an activity metric that varies primarily overthe range of 0-1%, and 100 observed trials of a behavioral task that thesubject gets right on 50% of occasions on average. The average percentcorrect trials can be computed for all of the measured trials thatfollowed a 5 second period when the measured activity metric was between0.2 and 0.3%. Similarly, the average percent correct can be computed forall other 9 increments from 0-1% for the activity metric. If there is acorrelation between the activity metric value and behavioralperformance, this may lead to a curve showing that at the low values ofthe activity metric, the subject got less trials correct on average,whereas at the high values, the subject got more trials correct onaverage.

[0505] Likelihood of success metrics can be computed separately fordifferent stimuli or behaviors. For example, one observed pattern ofactivity may correlate with a high likelihood of success for onestimulus or task, while a different pattern correlates with a highlikelihood of success for a different stimulus or task. Computing thelikelihood of success for both stimuli/tasks allows the selection ofwhichever stimulus or task is more likely to be successful at a givenmoment.

[0506] Using the relation between the activity metrics and percent ofpositive behavioral outcomes determined by the curve, which can often befit with a line, exponential, or logistical function, it may be possibleto predict the likelihood of success on a given trial using a givenstimulus from the value of an activity metric.

[0507] xiii. Combinations and Comparisons of Activity Metrics from theSame or Different ROIs

[0508] Another type of activity metric that may be computed arecombinations and comparisons of activity metrics from the same ordifferent ROIs. It is often useful to make comparisons between differentactivity metrics, or to compare the same activity metric for differenttime points, or time periods. All of the activity metrics describedabove can serve as inputs to combination and comparison functions suchas sums, averages, differences, and correlations. A useful comparisonmetric may be the difference between an activation metric for a recentperiod of time and the same activation metric computed for a referenceperiod of time, such as an earlier period of time. This value indicatesthe changing level of activation in an ROI. The difference can also becomputed between the average value of an activity metric computed fromone time period, such as the difference between the average of a metricfor all time points falling within a particular behavioral orstimulation condition, or for all time points falling within abackground or rest condition. Combinations can also be made betweenseparate activity metrics, including such as sums, averages,differences, and correlations. An example is the difference inactivation level between one ROI and another ROI at the same time point.This can be useful in indicating when one area is more active thananother, and can be used for training subjects in creating a higheractivity level for one area than another. Differences can also becomputed for different time points, which can be useful in determiningwhether one area is leading or lagging another area.

[0509] E. Displaying Activity Metrics

[0510] Activity metric data may be presented to the subject insubstantially real time using a display 180. In addition, these data maybe presented to a device operator on one or more additional displays.The resultant images may be presented in a variety of ways, as describedin the examples presented in the following section.

[0511] 2. Examples of Information Displays

[0512] As has been noted, an important aspect of the present inventionrelates to the provision of information to the subject as the subject'sbrain activity is measured in order to influence how the subjectperforms training exercises In one variation, information iscommunicated to the subject through computer generated displays whichthe subject is able to observe during training.

[0513] The information can relate to instructions, brain measurements,sensory stimuli, and training performance. Each of these different typesof information may be displayed by itself or in combination with othertypes of information.

[0514] The layout of the content of the information displayed can bewidely varied. For example, the information can be in graphical and/orin text form. The displayed information can include static images aswell as moving images, and optionally can also be accompanied by sound,or by other forms of sensory stimulation The subject or device operatorcan select multiple types of information that will be displayed togetherfrom among those described and depicted here.

[0515] Described herein are examples of what types of information may bedisplayed to assist the subject. Example display panels are shown inFIGS. 8-12.

[0516] A. Instructions

[0517] An important type of information that may be displayed to asubject is instructions. These instructions alert a subject regardingdifferent things that the subject is asked to do including perform atraining exercise, rest and other forms of response that may be asked ofthe subject. The instructions may be displayed concurrently with otherforms of information.

[0518] Moving visual images or a sequence of sounds or verbalinstructions or other means of communication can instruct the subject toperform ongoing sequenced behaviors, with each successive element in thesequence controllable based upon measured physiological activity.Provided herein in are examples of different instructions and ways ofcommunicating brain measurements that may be displayed.

[0519] B. Measured Information

[0520] Another important type of information that may be displayed to asubject is information relating to brain measurements. Provided hereinare examples of different brain measurements and ways of communicatingbrain measurements that may be displayed. This display may include rawanatomical brain image, raw functional brain image, moment-by-momentrepresentations of activity metrics, scrolling charts of the averagelevel of activity in a particular voxel or region of interest. Thisdisplay may also include performance measurements, including bothmeasurements of performance of an overt behavioral task, andmeasurements of performance of the subject's modulation of a region ofinterest.

[0521] C. Stimuli

[0522] Another important type of information that may be displayed to asubject is stimuli. Provided herein are examples of different ways ofcommunicating stimuli. Types of stimuli that may be presented includestatic or moving visual displays, tactile, proprioceptive or heatstimuli, odors, sounds, and other forms of sensory information.

[0523] D. Examples of Information Displayed

[0524] Many types of information may be presented, as will now bedescribed in detail.

[0525] One type of display panel is an Anatomy Section 10200. This panelmay present a T1, T2, or T2* weighted anatomical section of the subject.This section may be a coronal, sagittal, axial, horizontal view, or someother plane of section through the brain. This panel may also include ascale 10210 that indicates the correspondence between levels ofbrightness and measured values. This panel may be used for localizinganatomical structures, such as when the device operator uses anatomicalknowledge to look at one or more sections and determine the location ofrelevant anatomical structures. This panel may also be used for definingthe location for a region of interest. For example, once the deviceoperator has located an anatomical structure, he or she may selectpixels or select a bounded area on this display that will correspond toa region of interest. This display can also be used to compare withanother subject or a standard reference brain. For example, the deviceoperator may select sections of the subject's brain that correspond withknown locations defined in a reference brain such as described in theTalairach atlas brain or MNI reference brain. This operator may do thisby comparing images of the subject's brain with images of a standardbrain to find like structures. This may take place while the subject isin the scanner. This may be part of the process of determining a regionof interest. This region of interest may be used in the training of thesubject.

[0526] Another use of this panel is to present outlines of definedregions. These outlined defined regions can be used in defining a regionof interest for training. For example, if the device operator would liketo select Brodmann's area 4 as a region of interest, the software canoutline Brodmann's area 4 on the display, and the device operator canuse this information to select the appropriate voxels or area as theregion of interest. Anatomically defined regions can include any of theregions defined in a standard reference atlas such as the Talairachatlas or the MNI atlas. Defined regions can also include the savedregions of interest defined for the subject or for previous subjects orgroups of subjects. The display can show lines outlining a definedstructure. These defined regions when displayed can also be labeled onthe display according to their names. In addition, these defined regionscan be transformed into the appropriate space to match the anatomicalsection of the subject, and presented overlayed onto the subject'sanatomical section. This can be useful in localizing anatomical regionsin the subject, because it indicates which voxels in the current subjectcorrespond to defined structures in a reference brain. The process ofthis transformation, which can serve as the input to this display, isdescribed in Examples section 6.

[0527] Another type of display panel is an Anatomy Selector 10250. Thispanel may present controls usable by the subject or device operator toselect or manipulate the displayed anatomical section. These controlscan include controls for selecting the plane of section to display, suchas coronal, sagittal, and the position of the plane of section, andselecting the number of the scan plane within a scan volume, such as arostral, central, or caudal section. This panel may also includeadditional controls to adjust the brightness and contrast of the image,the ability to select the scaling and zoom and cropping of the image, toturn on and off subject information, and to make text or graphicalannotations on the section and mark regions of interest.

[0528] Another type of display panel is a Physiology section 10300. Thispanel may present an activation image as computed as output as describedin Examples section 1.B. This panel, and all anatomical andphysiological activity panels, may also show regions of interest 10310being used for training, or for measurement of an activity metric.Physiological activity panels may also present scales 10320 thatindicate the level of activity being presented, as well as a numericalunits scale, and may be color coded or intensity coded. One type ofactivation image that may be displayed is a correlation map 10400.Another type of activation image that may be displayed is a differencemap 10500. All of the types of computed activation images/volumes may beselected for presentation by the device operator or user using theselector panel, or pre-defined in the software.

[0529] One primary use of a physiology section panel is to allow thesubject or the device operator to select the area of a region ofinterest. This process is described in section 4. The subject or deviceoperator may use a pointing device to select a combination of voxels, orone or more bounded area corresponding to the region of interest. Byinspecting the physiology section, this selection can be made tocorrespond to activated or inactivated brain regions. This region ofinterest can then be used in subject training.

[0530] Another use of this panel is to present physiological resultsfrom a comparison brain or from an average of a group of brains, such asa standard brain, which may be used by the subject or device operator tomake comparisons to the physiology section. The physiological resultsfrom the standard brain may be transformed into the coordinate frame ofthe current subject using the same transform and methods described fortransforming an anatomical structure, as described in Examples section6. The device operator or subject may select a standard brain, and aphysiological activation condition from the standard brain, for displayby the software. The subject or device operator may then be able toselect voxels or bounded areas from the standard brain that had beenactivated by the current task, which they may use as a region ofinterest. Also, using this standard brain, the device operator orsubject may be able to find regions with higher or lower activation inthe subject than were observed in a standard subject performing asimilar task. Images or volumes may additionally be presented of asubtraction or other comparison of data collected for a standard brainor group of brains during a similar task from the current subject'sbrain, to highlight differences in activation patterns.

[0531] These comparisons may facilitate the localization of structuresfor use as a region of interest. These structures may be used as regionsof interest for training. Another use of this panel is to presentoutlines of anatomically defined regions overlayed onto physiologicalactivation patterns. This is very similar to the use of outlined regionsof interest just described for anatomical panels. These outlined definedregions can be used in defining a region of interest for training. Forexample, if the device operator would like to select Brodmann's area 4as a region of interest, the software can outline Brodmann's area 4 onthe display, and the device operator can use this information to selectthe appropriate voxels or area as the region of interest. Anatomicallydefined regions can include any of the regions defined in a standardreference atlas such as the Talairach atlas or the MNI atlas. Definedregions can also include the saved regions of interest defined for thesubject or for previous subjects or groups of subjects. The display canshow lines outlining a defined structure. These defined regions whendisplayed can also be labeled on the display according to their names.This can be useful in localizing anatomical regions in the subject,because it indicates which voxels in the current subject correspond todefined structures in a reference brain. The process of thistransformation, which can serve as the input to this display, isdescribed in Examples section 6.

[0532] Another type of display panel is an ROI map 10600. This panel maypresent any of the types of physiological activity maps with one or moreregions of interest overlayed. Each region of interest 10610 may bepresented in a different color or using a different line weight or linestyle. The regions of interest may be geometric shapes such asrectangles, circles, or elipses, or they may be selected from anarbitrary combination of pixels. The user may select regions of intereston these displays using a pointing device such as a mouse. Thisselection can take place either by selecting the corners of a regulargeometric shape such as a rectangle, or by selecting the center anddiameter of a circle or elipse, or by selecting individual voxels. Theregions of interest may be used to select areas from which additionalcomputations will be made, such as computations of activity metrics. Theregions of interest may be used in training a subject to modulate adefined region of interest.

[0533] Another type of display panel is a Subject information 10700panel. This panel may present any type of information about the subjectthat is being scanned or trained, or information about the scan session,such as Subject Name, Age, Weight, Scan Date, Scan Time, DeviceOperator, Goal of training, brain region being targeted.

[0534] Another type of display panel is a Text instructions 10900 panel.This panel may present instructions to a subject in text form. Theseinstructions may be for use in training, or in influencing the subjectto improve the course of training. For example, a subject may view thedisplay comprising the instructions and then perform training accordingto the present invention based on the instructions. These instructionsmay be commands for what a subject is intended to do in a task. Theseinstructions may be generated or selected by the software of thisinvention to control the subject's behavior. For example, the softwaremay monitor brain measurements, and determine instructions based on thebrain measurements. The subject may then view the display comprising theinstructions and perform training according to the present inventionbased on the instructions. These instructions may be generated by thedevice operator for presentation to the subject, typically duringtraining. For example, an instructor may input instructions, softwaretaking the instructions and causing them to be displayed to a subject,the subject then performing training according to the present inventionbased on the displayed instructions. The timing and content ofinstructions presented on this panel may be generated by the softwaredisclosed, as described in Examples section 3.

[0535] Another type of display panel may be a Movement information 11000panel. This panel may present information about the movement of thesubject, computed as described in section 6.B.iv. One item that thispanel may include is a trace of movement over time 11050. Another itemthat this panel may include is a motion scale 11100. Another item thatthis panel may include is a rotation scale. Another item that this panelmay include is translational motion 11200, indicating the motion ofvoxels in x,y,and/or z direction, or position in x,y,z direction.Another item that this panel may include is rotational motion 11300,indicating motion in roll, pitch and/or yaw. Another item that thispanel may include is a time scale 11400, indicating the time points ofeach measurement. This panel may scroll in time, so that with each newpoint presented, the older points move along so that a fixed period oftime before the present is always visible. Another item that this panelmay include is a trial indicator bar 11500. This may indicate somecomponent of a behavioral trial, such as the period of a stimulus orbehavior.

[0536] This movement information panel may be used by the subject tobecome aware of when he or she has moved within the scanner. Themovement information may allow the subject to realize that they need tobe more stationary. The movement information panel may also be used bythe device operator to realize that the subject has moved. This mightallow them to provide instructions to the subject to be more stationary,or to abort a trial, or a training session. This movement informationmay also be used to discard data from further processing if the movementexceeds a certain threshold.

[0537] Another type of display panel is an Image instructions 11100panel. This panel may present images meant to convey instructions to asubject. These images may constitute graphical icons known to thesubject to denote certain types of behavior. For instance, they maycontain images indicating a body part to move, or to imagine moving.These images may be selected by the data analysis/behavioral controlsoftware 130, as described in Examples section 3. This presentation ofimage instructions may be useful in instructing the subject. Inparticular, the presentation of images may be useful in instructing thesubject based upon the brain activity metric measured for the subject,and this may further be useful in guiding subject training. An imageinstructions panel also has all of the uses described for a Textinstructions 10900 panel.

[0538] Another type of display panel is a Video instructions 11200panel. This panel may present video, or moving images. These movingimages may constitute instructions for the subject For example, thesubject may be instructed to perform actions, or imagine actions, inaccordance with what the subject sees on the video. For example, if thevideo shows the sequential movement of each finger on the hand, thesubject may use this as an instruction to perform those movements. Thesevideos may constitute graphical icons known to the subject to denotecertain types of behavior. These videos may be selected by the dataanalysis/behavioral control software 130, as described in Examplessection 3. This presentation of video instructions may be useful ininstructing the subject. In particular, the presentation of video may beuseful in instructing the subject based upon the brain activity metricmeasured for the subject, and this may further be useful in guidingsubject training. A video instructions panel also has all of the usesdescribed for a Text instructions 10900 panel.

[0539] Another type of display panel is a Reward information 11300panel. This panel may present information to the subject regarding hisor her success in training. The computation of information presented onthis panel is described in section 6.C. and 6.D. One type of informationthat may be presented on this panel may be whether a subject wassuccessful on the most recent trial. Another type of information thatmay be presented on this panel is the level of activity or an activitymetric achieved for some period of the most recent trial. Another typeof information that may be presented on this panel is the subjectssuccess or failure at the most recent behavioral trial if the subject isperforming concurrent behavioral trials. Another type of informationthat may be presented on this panel may be the target level ofactivation or an activity panel metric that the subject was supposed toreach. Another type of information that may be presented on this panelmay be the challenge level that the subject is at, corresponding to thelevel of difficulty, or degree of modulation of the region of interest.Another type of information that may be presented on this panel may bewhether the difficulty will increase or decrease on the next trial.Another type of information that may be presented on this panel may be atime-out period indicating that the subject has performed a trialincorrectly and will have to wait a period of time before the next trialas a punishment. Some or all of these types of information may be usefulin rewarding the subject for performing trials correctly, or punishingthe subject for performing trials incorrectly. The subject may view thisinformation to gauge their performance, and may continue or change theirstrategy and effort level accordingly. This may be beneficial intraining the subject.

[0540] Another type of display panel is a Behavioral %correct 11400.This panel may present information regarding the subjects behavior on aconcurrent behavioral trial such as a visual discrimination task thattakes place during training. Another type of information that may bepresented on this panel may be the overall percent of trials that thesubject has been successful on. Another type of information that may bepresented on this panel may be the percent correct for each of a seriesof different stimuli or behavioral conditions. Another type ofinformation that may be presented on this panel may be the standarderrors or standard deviations of performance for each of a series ofdifferent stimuli or behavioral conditions. The subject may view thisinformation to gauge their performance, and may continue or change theirstrategy and effort level accordingly. This may be beneficial intraining the subject. These types of information may all be useful inbehavioral training of a subject, and/or in concurrent training of thesubject to modulate a brain region.

[0541] Another type of display panel is a Brain % correct 11500 panel.This panel may present information regarding the subject's successfultrial performance in modulating the activity of a defined brain region.One type of information that may be presented on this panel may be theoverall percent of trials for which the subject was able to achieve alevel of an activity metric higher than the target level. Another typeof information that may be presented on this panel may be the percent oftrials for which the subject was able to achieve a level of an activitymetric higher than the target level for each of a group of stimuli.Another type of information that may be presented on this panel may bethe threshold for the subject to achieve a certain percentage ofsuccessful trials. Another type of information that may be presented onthis panel may be the standard errors or standard deviations of thepercent of successful trials for each stimulus. These types ofinformation may all be useful in training of the subject to modulate abrain region. The subject may use this information to gauge theirperformance, and may continue or change their strategy and effort levelaccordingly. Another type of information that may be presented on thispanel may be icons 11510 for each of the different types of trials, suchas stimuli or behaviors. The subject may select these icons using apointing device to indicate the type of stimuli or behaviors that thesubject would like to engage in, or the type of stimulus of behavior tobe used in a next trial.

[0542] Another type of display panel is an ROI Activity 11600 panel.This panel may present the level of an activity metric measured for adefined region of interest. One type of information that may bepresented on this panel may be the trace of the activity metric 11610measured over some period of time for the region of interest. This mayconstitute a scrolling panel such that as each new value of the activitymetric is computed. The chart values may take positions to show all themost recent values, such as the most recent 100 seconds. Another type ofinformation that may be presented on this panel may be a markerindicating the most recent value of the activity metric 11620. Anothertype of information that may be presented on this panel may be anindicator of period of one or more behavioral trial 11630, such as anindicator of when some period of a trial was taking place, such as theperiod of a stimulus, behavior, or activiation. Another type ofinformation that may be presented on this panel may be a target 11640indicating the level of activation that the subject is instructed toreach on a particular trial. Another type of information that may bepresented on this panel may be a scale of values of the activity metric11650. Another type of information that may be presented on this panelmay be a timescale of values of the activity metric 11660. The valuesused for activity metrics can correspond to any value computed for anactivity metric. The computation of these values are described inExamples section 1.D. Multiple copies of an ROI Activityl 1600 panel maybe present at the same time, allowing comparison of the level ofactivity between different activity metrics. These may include a traceof the activity metric measured from a background or alternate region ofinterest 11700. This may provide an indication of an activity metricfrom a brain region not undergoing training. Another trace that may bepresented is a trace of the difference in activity between the region ofinterest undergoing training and a background region of interest 11800,or a difference between the activation pattern for the current subjectand some other subject or a reference subject. Panels 11700 and 11800may include all of the same features as described for 11600. Thesepanels may be useful in determining the state of activity in a localizedbrain region in a subject. These panels may also be useful in guidingtraining of a subject. These panels may also be useful in guidingperformance of a subject. These panels may also be useful in determiningwhen a subject will be most likely to perform a trial or tasksuccessfully. The subject may view this information to gauge theirperformance, and may continue or change their strategy and effort levelaccordingly. This may be beneficial in training the subject.

[0543] Another type of display panel is a PETH 11900 panel. This panelmay present a peri-event time histogram metric. The computation of thesemetrics is described in Examples section 1.D.xi. One type of informationthat may be presented on this panel may be a trace of the peri eventtime histogram. Another type of information that may be presented onthis panel may be a trace of the PETH +/− standard errors. Another typeof information that may be presented on this panel may be a trial barindicating time periods from a trial. Another type of information thatmay be presented on this panel may be a scale of values of the PETH.Another type of information that may be presented on this panel may be atimescale of values of the PETH. These panels may be useful to thesubject and device operator in determining the state of activity in alocalized brain region in a subject. These panels may also be useful inguiding training of a subject. These panels may also be useful inguiding performance of a subject. These panels are also useful indefining a region of interest as described in section 4.

[0544] The various panels described may change in the information thatthey present from moment to moment. An example of this is depicted inFIG. 10. FIG. 10 shows the same panel, an ROI Activity panel, at 5different time points during a single trial. The trial lasts 60 seconds,and begins at start time 0, shown in 12010. At this point, the subject'sdisplayed activity metric happens to be fairly low as seen in 12011, andthe subject is seen to be at the end of a task period, entering a restperiod, as seen in the task indicator bars 12012. At time=15 s in panel12020, the chart of the activity metric has shifted left by 15 s as newdata has been collected and processed. The subject's activity metriccontinues to be low. At time=30 s in panel 12030, the subject may beinstructed to activate a brain region using a defined task, and toachieve a level of the activity metric above the performance targetindicated by the horizontal bar 12031, which thereby supports a form ofinstruction and also serves as an indicator of the subject's pastperformance. At time=45 s as shown in panel 12040 the subject's activitymetric is still up, as intended. The subject may be presented with astimulus, which may further increase the level of the metric. At time=60s in panel 12045, the performance target bar may disappear, and/or thesubject may be instructed to rest. The entire trial 12046 may last 60 s,and the task period during which the subject activates a brain regionmay last 30 s. At this time, the next trial is begun. Repeating trialsmay constitute training of the subject. Continued performance oftraining may constitute exercise. It should be noted that this examplerepresents only one form of trial. In particular, the durations,ordering, and number of each type of time period, instruction, stimulus,display or other component may vary for different types of trials.

[0545] Another type of display panel is an Average change per trial12050 panel. One type of information that may be presented on this panelmay be the difference in an activity metric between two periods in atrial, such as between a stimulus or behavior and a background period.Another type of information that may be presented on this panel may bethe average difference in an activity metric between two periods in atrial across several trials, such as between a stimulus or behavior anda background period. Another type of information that may be presentedon this panel may be the standard error of this difference. Another typeof information that may be presented on this panel may be a timescale ofwhen the trials displayed took place in sequence. Another type ofinformation that may be presented on this panel may be a magnitude scaleof the size of the difference measured. These panels may be useful indetermining the change of activity in a localized brain region in asubject between conditions. These panels may also be useful in guidingtraining of a subject. These panels may also be useful in guidingperformance of a subject. The subject may view this information to gaugetheir performance, and may continue or change their strategy and effortlevel accordingly. This may be beneficial in training the subject.

[0546] Another type of display panel is a Stimulus selector 12100 panel.This panel may present icons representing stimuli or behaviors 12100.The subject or device operator may select these icons using a pointingdevice such as a mouse to select a stimulus or behavior that will beused for training, or that will not be used for training. The subject ordevice operator may select these icons using a pointing device such as amouse to select a stimulus or behavior that will be used for the nexttrial, or that will not be used for the next trial. This panel caninclude all of the types of information described for panel 11500.

[0547] Another type of display panel is a Ready? 12200 panel. This panelmay present an indicator which designates that a next trial is ready, orthat asks the subject or device operator when they are ready to beginthe next trial. The subject or device operator can then be made awarethat a trial is ready to begin. The subject or device operator can alsooptionally use a pointing device or other means of indicating when theyare ready to begin a trial. This can be used in aiding a subjectsperformance of tasks, or aiding a subject in training as described inthis invention.

[0548] Another type of display panel is a Stimulus images 12300 panel.This panel may present visual stimuli to the subject. These visualstimuli may be selected as described in Examples section 3. The subjectmay use this display panel to observe and perceive the presented stimuliin accordance with the remainder of this invention. These stimuli mayinclude, for example: 1) photos of faces, 2)photos of objects, 3) photosof the subject, 4) checkerboard stimuli, 5) sin wave or square wavegratings, 6) other types of visual stimuli as described in thephysiology and psychological literature. These displays may be used toselectively stimulate activation of defined regions of the subject'sbrain. These displays may be used as the basis of selection inpsychophysical or cognitive behavioral tasks, such as tasks in which thesubject must make a selection between different stimuli based upon adefined characteristic. For example, the display may present a nearlyvertical grating stimulus, with the subject being required to indicatewhether the stimulus was exactly vertical or not. The stimuli presentedmay enable a two alternative choice task, in which two stimuli arepresented, and the subject selects one of the stimuli that possesses adefined feature, such as being an exactly vertical grating as opposed toa slightly tilted grating. These displays may be used as an aid insubject training, including by activating certain brain regions.

[0549] Another type of display panel is a Stimulus video 12400 panel.This panel may present video for use in visual stimulation. The subjectmay use this display panel to observe and perceive the presented stimuliin accordance with the remainder of this invention. These visual stimulimay be selected as described in Examples section 3. These stimuli mayinclude: 1) moving images, 2) cinematographic material, 3) 3-D virtualreality material that simulates a 3-D environment, 4) stimuli designedto stimulation visual motion areas, 6) other types of moving stimuli asdescribed in the physiology and psychological literature. These displaysmay be used to selectively stimulate activation of defined regions ofthe subject's brain. These displays may be used as the basis ofselection in psychophysical or cognitive behavioral tasks. Thesedisplays may be used as an aid in subject training, including byactivating certain brain regions. For example, the display may present anearly vertical moving grating stimulus, with the subject being requiredto indicate whether the motion was exactly vertical or not.

[0550] Another type of display panel is a VR stimuli 12500 panel. Thispanel may present virtual reality stimuli, such as stimuli designed tosimulate a 3-D experience for the subject. This panel may have twosides, one viewed by each eye to form a stereo image.

[0551] Another type of display panel is a Success analogy 12600 panel.This panel may present an analogy of the subject's level of success on acurrent trial. This analogy may be used to indicate the level of anactivity metric. The computations of values for activity metrics aredescribed in Examples section 1. Examples of success analogies that maybe used to indicate the level of an activity metric include:

[0552] 1) Bars that increase in length in proportion to the measuredlevel

[0553] 2) Polygons that increase in size in proportion to the measuredlevel

[0554] 3) Scrolling charts of the measured level over a period of time

[0555] 4) Scrolling charts of the rolling average of the measured level

[0556] 5) Computer games that move more quickly or more slowly, or that‘succeed’ in their goal in proportion to the measured activity level

[0557] 6) Sounds that indicate the presence of a particular measuredlevel

[0558] 7) Sounds that are proportional to the measured level in someparameter, such as pitch or amplitude

[0559] 8) Colors that change in proportion to the measured levelaccording to a color map

[0560] 9) Objects that move at an apparent speed related to the measuredlevel

[0561] 10) Movie images

[0562] 11) Objects that assume a position related to the measured level

[0563] 12) Objects that move at a speed related to a measured level

[0564] 13) Conceptual ‘success analogies’ such as the level to which aweight lifter has lifted a weight being correlated with the level ofactivity in a brain region

[0565] 14) Metrics can also be presented using auditory cues such as thepitch, frequency, intensity or repeat rate of sounds.

[0566] These success analogies are useful in indicating a subject thelevel of an activity metric. The subject can view the success analogypanel in order to quickly grasp the level of success or activation thatthey are achieving. The subject can choose which type of success analogyis the most helpful in getting a sense of their success level. Thesepanels are therefore useful in training a subject. They can also beuseful in enhancing motivation in a subject.

[0567] Another type of display panel is a Brain image saggital 12700panel, a Brain image coronal 12800 panel, and a Brain image axial 12800panel. These panels may present aligned images of anatomical orphysiological sections through the brain. The alignment bar 12710, whichmay be present on any of these panels, may indicate the position ofsection of the other panels with respect to the present panel. Thesubject or device operator may select the position of the alignment barto select a new section. By selecting the position of the alignment bar,the user can choose what section will be presented, for eitheranatomical or physiological section displays. If the user selects theposition of the alignment bar on one section to reflect the position ofa new plane of section, this may alter what sections are displayed onthe remaining to of the three planes of section to correspond to planesat that level. This is useful in selecting sections for defining regionsof interest, for substantially real time selection of ROIs, and foraiding in subject training.

[0568] Another type of display panel is a 3-D brain transparent 13000panel, 3-D brain rendered 13100 panel, or a 3-D brain mesh 13200 panel.These panels may present 3-D views of the subject's brain using avariety of algorithms These algorithms are described in the manuals andliterature describing existing fMRI/MRI data analysis packages. Thephysiological activity of the subject as measured through an activationvolume as described in Examples section 1.B. may be depicted in threedimensions. In particular, activation regions or ‘blobs’ may besuperimposed upon 3D images of the brain, or presented so as to showtheir internal positions relative to the 3D structures as will befamiliar to one skilled in the art. In addition, the physiologicalactivity may be overlayed onto the anatomy of the subject. Thesedisplays may be made either in the coordinate space of the subject, orin a standard coordinate space such as Talairach space or MNI space.These displays may be useful in localizing regions of interest in threedimensions, or in 3-D in substantially real time. These displays may beuseful in determining areas of activation in a subject in 3-D and/or insubstantially real time. The subject or device operator may observethese displays to determine the regions activated by a task. The subjector device operator may observe these displays to localize a region ofinterest for training.

[0569] Another type of display panel is a Brain section montage 13300panel. This panel may present the data described for panels 12700-12800on a single panel, as well as including controls to allow the user ordevice operator to rotate the brain image, zoom in and out, and selectsections. These selections may be used to update the views shown inother panels corresponding to the same brain. This may be useful inlocalizing regions of interest and in training subjects. The subject ordevice operator may interact with this panel to select the viewpresented of the brain data. This selection may apply throughout thedisplayed panels, or only to certain panels.

[0570] Another type of display panel is a Training progress indicators13600 panel. This panel may present indicators of the progress throughtraining, such as the number of trials completed, the number remaining,and the time remaining. The subject and device operator can view thispanel to determine the progress through training. This can be useful inmaintaining the motivation of the subject, and in training.

[0571] Another type of display panel is a Behavioral choice 13500 panel.This panel may present choices for a subject, and allow the subject toregister responses. These choices may be choices for the subject to makeduring a concurrently presented behavioral task. For example, if thesubject is engaged in a two alternative sequential task, the panel maypresent the subject with the two choices to select from. The subject mayuse this panel to select with a pointing device such as a mouse or ajoystick which choice they would like to make. This may be useful inbehavioral training. This may also be useful in training of brainactivation patterns.

[0572] E. Combinations of Information Panels

[0573] It is noted that one or more different types of informationpanels may be displayed simultaneously or sequentially. For example,display panels comprising one or more combinations of different types ofinformation including, for example, instructions, physiologicalmeasurement related information, subject performance relatedinformation, and stimulus information, may be simultaneously displayed.Alternatively, panels of different types of information may bedisplayed.

[0574] By displaying multiple different types of information at the sametime or sequentially, different methods according to the presentinvention may be performed and facilitated. In particular, the subjectcan be instructed regarding what to do as well as how well the subjectis doing during training. For example, by displaying behaviorinstructions with subject performance related information and/orphysiological measurement related information, the subject can beinformed regarding his or her performance as the subject performs thetraining.

[0575] 3. Selection and Triggering of MeasuredInformation/Stimuli/Instructions

[0576] A key element of the current invention regards the generation ofinformation, and the selection of stimuli or instructions to bepresented to a subject, as well as the timing of when this presentationwill take place. This selection may be made by performing computationson the activity metrics defined above in Examples section 1.D.Selections can be made from a predefined set of stimuli or instructions,or stimuli or instructions can be generated de novo. The inputs to thisprocess are one or more of the activity metrics described, plus one ormore sets of instructions or stimuli, and optionally plus measurementsof a subject's behavior in cases where this is being measured. For thisselection process, in some instances one or more stimuli are selectedalone, and no instruction is given In another example one or moreinstructions are selected alone, and no other form of stimulus is given.In another example, stimuli and instructions are tied together inpre-defined pairs, and one or more pair is selected together. In anotherexample, one or more stimulus and one or more instructions are eachselected independently.

[0577] The methods of selection and presentation for stimuli and forinstructions are conceptually similar, and they will be explainedtogether. For instance, their might be a set of ten visual stimuli, orten visual images corresponding to instructions to imagine a movement.In either case, the same algorithm could be used to select from amongthe ten, and the same display means could be used to present them to thesubject. However, stimuli or behaviors used and the means of selectionmust, of course, be appropriate to the goal being sought. This processof generating information for stimulus or behavior selection may beintegrated into the various methods of the present invention. Forexample, the methods may include accessing a subject's likelihood ofsucceeding at a training activity; and communicating an instructionbased on the assessed likelihood.

[0578] A. Random Selection

[0579] One example of selecting a stimulus is random selection. It maybe desired to randomly intermix different stimuli or instructions forbehavior. This may be done, for example, when more precise control ofthe training stimuli is not required, and serves as a default methodRandom intermixing may also be used to prevent habituation of neuralresponses that can take place if the same stimulus or behavior ispresented repeatedly on successive trials. In such instances, thestimulus or behavior to be employed for each trial may be selected fullyor partially at random from the stimulus set.

[0580] B. Selection Based Upon an Activity Metric

[0581] Another example of selecting a stimulus is stimulus selectionbased upon an activity metric measured from a region of interest. Inthis example, stimuli may be selected based upon the level of anactivity metric. For example, each of a set of stimuli may be assignedto one range of the activity metric, so that if the activity metric iswithin this range then that stimulus will be presented. For example, ifthe activity metric varies approximately evenly from 0-1% over time,then each one of ten stimuli might correspond to a range of 0.1% of therange in the activity metric, from 0-0.1% for the first stimulus, up to0.9-1% for the last stimulus. At the moment that a stimulus should bepresented to a subject, the activity metric value is measured, and thestimulus is selected whose range corresponds to the measured value. Ause for this method in training is that some stimuli are morechallenging than others, and this method can match the more challengingstimuli to the periods of higher (or lower) activation of a region ofinterest involved in the perceptual processing of those stimuli. Oneexample of this use is that overall trial performance can be improved ifactivation metrics are used to select stimuli or behaviors. Subjects canperform tasks more effectively, learn and remember more effectively, andundergo more effective and more rapid learning and training when theappropriate stimulus or behavior is selected for the observed value ofthe activity metric for a relevant region of interest.

[0582] Another example of selecting a stimulus is stimulus selectionbased upon a likelihood of behavioral success metric. The use of thesemetrics to select stimuli and instructions can also be used to helpsubjects to perform tasks more effectively, learn and remember moreeffectively, and undergo more effective and more rapid learning andtraining. If a likelihood of behavioral success metric has been computed(as explained above in Examples section 1.D.xii.) for each of two ormore stimuli, then at different moments, the likelihood of successmetric will be different for each of the stimuli. Stimuli may beselected based upon the stimulus with the highest current likelihood ofsuccess metric given the current activity metric. However, the overalllikelihood of success metric may be higher for one of the two stimuli,so it may be preferable to use a measure of the difference between thecurrent likelihood of success and the average likelihood of success foreach stimulus. This way, the stimulus will be selected whose likelihoodof success is the most elevated from its average level. Using likelihoodof success metrics can improve the overall performance of subjects inperforming tasks, and in behavioral training, because subjects are, onaverage, presented with stimuli and tasks that they are more likely tosucceed with at the moment that they are presented.

[0583] Another example of selecting a stimulus is selection based upon aspatial pattern comparison metric. A target pattern may be selected.This target pattern may correspond to the average pattern activated byeach stimulus or behavior. The target pattern may correspond to thepattern measured for successfully completed trials or for unsuccessfultrials for a given stimulus or behavior. When a spatial patterncomparison metric reaches a target level of similarity between theobserved pattern and the target pattern for a given stimulus orbehavior, then that stimulus or instruction is presented. This can beused to present stimuli or instructions when the subject is most likelyto successful with that stimulus or task.

[0584] Another example of selecting a stimulus is selection based upon aperformance target level. A stimulus that may be presented to thatsubject is a representation of the performance target that the subjectis supposed to achieve. The level of the target presented may beselected based upon the computed level of a performance target. Aperformance target may be presented, for example, on an ROI activitypanel 11600. Other kinds of stimuli may also be selected based upon aperformance target. For example, different stimuli or sets of stimulifrom a stimulus set may be associated with different levels of aperformance target. Some stimuli may be more challenging to perceive ordiscriminate, and these may be associated with higher or lower values ofthe performance target. For example, when the performance target ishigh, the subject is presented with more challenging stimuli.

[0585] C. Selection by the Subject or Device Operator

[0586] Another example of selecting a stimulus is selection by thesubject or the device operator. Through observing the conducting oftrials, and the resultant activity maps and activation metricsdisplayed, the subject or device operator may form an opinion as to whatstimulus will be best. Either the subject or the device operator mayselect the stimuli or behaviors for use from the selected stimuli orinstructions for behavior set, using one of the display panels designedfor the purpose, such as shown in 11500, 12100. This process maycomprise having a subject perform a plurality of trials involvingdifferent stimuli and/or behaviors, measuring and displaying activitymetrics during the plurality of trials, having the subject select one ormore of the different stimuli and or behaviors to perform on a futuretrial based upon a review of the measured activation from the pluralityof trials.

[0587] D. Creating a Stimulus or Behavior Continuum Corresponding to aLevel of Activation

[0588] In another example, stimuli or behaviors are created de novoalong a pre-defined continuum described by one or more parameter. Thatcontinuum is formed into a correspondence with levels of an activitymetric that allows automated choice of the one or more parameter thatdefines the stimuli based upon the activity metric level as measured ator just before the time that a stimulus should be presented to thesubject. For example, given a visual sin wave grating stimulus that canhave any period based upon a parameter that varies from 0.1-1cycles/degree, and an activity metric with continuous values from0.1-1%, a sin wave grating stimulus can be created de novo based uponthe value of an input parameter (cycles/degree) corresponding to thelevel of an activity metric. Stimuli with a higher value of thecycles/degree parameter may be more challenging to perceive ordiscriminate, so it may be useful to select those stimuli at times ofhigher measured activation for a region of interest involved inperceptual processing of the visual stimuli. This can also be done forinstructions. For instance, a smooth continuum in the location of thetarget of a pointing exercise can be made to correspond to the level ofan activity metric in a brain area involved in the generation of thismotor behavior.

[0589] E. Identifying when to Begin a Trial

[0590] It is often desirable for a subject to begin a particular trialor part of a trial, or receive a stimulus or engage in a particularaction, or training exercise, at a moment that is determined based uponthe measured physiological activity up to that point. The dataanalysis/behavioral control software 130 can function to select timepoints for initiation of a trial when a particular activity metric is ata high or low value, or crosses a threshold value. Subjects can performtasks more effectively, learn and remember more effectively, and undergomore effective and more rapid learning and training when trials arebegun at times when the observed value of the activity metric for arelevant region of interest is above a threshold value.

[0591] Another example of identifying when to begin a trial is beginninga trial when an activity metric measured from a region of interestinvolved in mediating a task being performed by a subject has reached acriterion level, such as a criterion activation level. For example,subjects can perform more effectively at a behavioral task if the starttime for task trials is selected based upon the level of activation forthe brain regions of interest involved in mediating that task reaching athreshold. If a subject is performing a visual discrimination taskinvolving representation by a particular sub-region of the visual cortexsuch as a motion detection task using randomly moving dots, then visualdiscrimination trials may be initiated when an activity metric measuringthe level of activation for this sub-region of interest reaches acriterion level, such as an activation criterion level reached by a thesub-region of visual areas V1 or MT that mediates visual perception ofthe visual area corresponding to the position of the dots.

[0592] Another example of identifying when to begin a trial is beginninga trial when an activity metric measured from a region of interestundergoing training by a subject has reached a criterion level, such asa criterion activation level. If a subject is performing a motor taskinvolving a particular sub-region of the motor cortex, or is beingtrained to activate that region of the motor cortex, then trials may beinitiated when an activity metric measuring the level of activation forthis sub-region of the motor cortex reaches a criterion level.

[0593] Another example of identifying when to begin a trial is beginninga trial when an activity metric measured from a region of interest hasreached a criterion level, such as a criterion likelihood of successlevel. For example, as assessed using a likelihood of success metric,subjects may be able to perform a task more effectively when the task isstarted at times that are selected because a likelihood of successmetric as defined above in Examples section 1 has reached a thresholdvalue. For example, if a subject is performing a visual discriminationtask such as a motion detection task using randomly moving dotsdescribed above, and a measure of the average likelihood of success atthe task has been determined for each of several levels of activation ina sub-region of the visual cortex involved in mediating the task, thenthe task may be begun when the level of activation of the measuredregion of interest corresponds to a criterion likelihood of success inperforming the task. Likelihood of success metric computation isdescribed further in Examples section 1.

[0594] Another example of identifying when to begin a trial is beginninga trial when an activity metric measured from a region of interest hasreached a criterion level, such as a spatial pattern comparison metric.A target pattern may be selected, and an activity metric may be computedthat measures the similarity of this target pattern with the currentlyobserved pattern, as described in Examples section 1. A trial may bebegun when this metric reaches a criterion level. The target pattern maycorrespond to the average spatial activation pattern measured for theregion of interest during successful trials. When a comparison metricthat measures the dot product between the target pattern and the currentpattern reaches a threshold value, a trial may be instigated. This canbe used to present stimuli or instructions when the subject is mostlikely to be successful or have a positive outcome for a stimulus ortask. Therefore, this can be used to facilitate successful training andexercise.

[0595] F. Identifying when to Provide Training Reinforcement

[0596] As training is performed, it is advantageous to provideinformation to the subject to reinforce their training efforts. Forexample, when a subject reaches a target level of performance, it isadvantageous to provide this information to the subject. In oneembodiment, software communicates a message of positive reinforcement(e.g., Good job!) when a desired level of activation is achieved. Inanother embodiment, software communicates a message of negativereinforcement (e.g., Focus!, or Time for a break?) when the subject'sactivation is not at a level that is desired or would be expected.

[0597] 4. Modes of Communication with a Subject

[0598] A variety of different modes of communication can be used torelay information between the subject and another party, for example amedical professional. For example, information may be communicatedbetween people, transmitted through a direct electrical connection to anearby point, or through a connection mediated by land-line or wirelesstelecommunications equipment or the internet. Various examples of howinformation may be communicated in the system of the present inventionare provided below.

[0599] A. Two way Audio and/or Video Communication

[0600] According to this variation, the voice of the subject is pickedup using a microphone within the apparatus, transmitted, amplified, andplayed to the device operator or other healthcare professional, eithernearby or distant. This recording can be turned off automatically ormanually during the process of scanning. The voice of the deviceoperator or other healthcare professional is picked up using amicrophone, transmitted, amplified, and played to the subject. In someinstances, one-way or two-way video communication is also used byimaging the patient in substantially real time and presenting the imageto the device operator or other healthcare professional, or imaging thedevice operator or other healthcare professional and presenting theimage to the subject in substantially real time on the monitor viewed bythe subject.

[0601] B. Subject Control of Computer Interface

[0602] According to this variation, a computer interface is providedthat allows the subject to input information. A wide variety of inputdevices are known, including, but not limited to computer joystick,mouse, trackball, keyboard, keypad or touch-screen, a botton-box withresponse buttons that the subject can press, game controller devices,and other computer interface means. These devices can also allowedshared control of a pointer or cursor on a computer with a pointingdevice controlled by the device operator, such that either device can beused to control the pointer or cursor.

[0603] 5. Sound Cancelling Headphones

[0604] In order to increase patient comfort within the scanner, whichcan be loud when operational, subjects may be provided with soundcancelling headphones. These headphones can be used to produce anopposite waveform to the sound produced by the scanner. This can beaccomplished by using a microphone close to the subject to measurerecorded sound, and providing an appropriately amplified complementarysignal to defeat the sound heard by the subject. Equipment designed forthe purpose is, for example, the Instructioner produced by ResonanceTechnology, CA.

[0605] Sound cancellation can also be accomplished by providing anamplified, digitized, pre-recorded waveform to the subject that issubstantially the opposite of the repeated sound waveform produced bythe scanner. The subject or device operator is then allowed to adjustthe delay of this repeated signal with respect to the scanner noise andthe amplification of this signal so as to produce the maximal soundcancellation.

[0606] This signal may be presented using either headphones worn by thesubject, or using headphones or earplugs with sound-conductive tubingthat lead sounds to the subject's ears from a speaker outside of themeasurement apparatus.

[0607] 6. Localization of Structures Using Standard Coordinates, andCoordinate Transforms

[0608] This section describes several ways in which one may localizeregions of interest from on physiological scan data. If a givenanatomically-defined region is to be used as the region of interest fora subject, software may be used to select the voxels of a givensubject's physiological and anatomical brain scanning volumecorresponding to that anatomically-defined region. This selection maytake place in substantially real time. For example, the user may selectan anatomical region of interest from a pre-defined database ofanatomical regions. Software may then be used to determine the voxelswithin the physiological or anatomical scans of the subject thatcorrespond to the selected structure. The software can also highlightthe structure, draw an outline around it in 2-D or 3-D representationsof the subject's brain, and label the structure. The software can alsobe used to label all structures on a given section of the subject'sbrain, or all structures that match a selected criterion, such as allcortical areas. The software can also use custom anatomical boundariesdefined by the user, which can also be added to this database. Examplesof this functionality are shown in FIG. 9.

[0609] The first step in this process is for the device operator toselect the anatomical area of interest from a standard coordinate systembrain, such as the Talairach Atlas or the MNI Atlas with correspondingcoordinate system. The device operator can do this by using a textdesignation of the area of interest (such as a particular Brodmann'sArea). This text designation can be either selected from a pull-downmenu of pre-defined choices corresponding to the anatomical areas takenfrom an atlas plus user-defined areas, or entered as free text. Thistext designation is searched from a database of which voxels correspondto which anatomical areas to produce a list of corresponding voxels.Additional areas defined in the same way can be added to create acombined area, or subtracted to create a difference area. Alternatively,the user can select the region of interest from one or more planes of ananatomical map in standard coordinates. These selected voxels from thestandard brain can be saved to disk as a brain volume mask, or as a listof voxel points, and used at the time of scanning.

[0610] The transform from standard coordinates to the coordinates of aparticular subject being measured must then be defined. This takes placeby the user designating a variety of points on the subject's brain thatwill be used to correspond these points to the pre-defined standardcoordinate brain, as shown in FIG. 9a. The first point selected willnormally be the anterior commissure, shown on a mid-sagittal section.The program will assume that the subject's brain is identical to thestandard coordinate brain, and present on the display the pointcorresponding to the anterior commissure in a standard brain as a targeton top of the section of the subject's brain as a background, while alsopresenting text designating the name of the structure: “anteriorcommissure”. The device operator can select a different section as thebackground section. The device operator then mouse-clicks the point ofthe anterior commissure on the actual section of the brain of thesubject as seen in the background section. The program will take in thepoint of the anterior commissure in 3-D coordinates, so that it can becompared with the reference brain point. The difference in positionbetween the point in the standard coordinate brain and the pointmeasured for the subject's brain is added to subsequent points beforethey are displayed to the subject, to shift the display point to becloser to that observed for the subject. The program will then gothrough a variety of additional points in succession and present targetsfor the point on the subject's brain; the user will select the point ofthe anatomical location on the subject's brain; and the program willtake in this data. The targets are used so that the user may morequickly select each corresponding point on the subject's measured brainvolume, without reading a text description of the relevant area toselect. The points used will include: anterior commissure, posteriorcommissure, occipital pole, frontal pole, rostral pole (normally allselected on a mid-saggital section), left and right extremes of brain(normally selected on a coronal or axial or horizontal section).Additional points can be used for an even better fit. Once the locationsof all of these points in the standard coordinate brain, and in themeasurements for the subject's scan volume, the 3-D to 3-D affinetransformation is computed using standard methods that produces theleast-squared error in transforming the points in the standardcoordinate brain to the points in the subject's observed brain volume.This transformation takes into account translation, rotation, andscaling to locate corresponding points within the subject'sphysiological or anatomical scanning volumes with those from thestandard coordinate brain. This transformation will be used to make thecorrespondence between all other points. This process can take placewhile the subject is in the scanner, in a matter of seconds or minutesfrom the time the data is actually collected, and using the samecomputers and software used in the scanning and substantially real timedata transformation procedures.

[0611] If necessary, more complex transforms can be computed, includinginternal morphing to allow more precise correspondence between definedanatomical points within the two structures with interpolation of thecorrespondences of points intervening between the defined anatomicalpoints. Also, the transformation can take place by automaticregistration of brain volumes (see for example methods described inSPM99 and other existing MRI/fMRI/PET data processing packages).

[0612] Once the transformation has been determined, any point in thestandard brain can be translated to find the corresponding point(s) inthe subject's brain scan volume, and vis. versa.

[0613] Therefore, a volume mask is generated corresponding to everypoint in the subject's brain volume that corresponds to a point from theanatomical structure(s) selected by the device user. This volume maskcan be overlayed upon the subject's brain images to allow the user tomore easily and accurately select the location of a region of interest,or the volume mask can be used as a region of interest itself.

[0614] Each voxel in the subject's brain can be assigned a fractionalprobability of being within a defined brain structure. To do this, allof the points from the standard brain that correspond to a given pointin the subject's measured brain volume are determined, along with thefraction of overlap, which is used as a weighting factor. The fractionalprobability of being within a given structure is then determined as thesum of (the product of each corresponding pixel's being within thatstructure as determined from existing atlas data, times that pixelsweighting factor.)

[0615] The software can function in the reverse direction, providing aspatial readout of the location in standard coordinate space of a givenlocation in the brain of a subject selected by the device operator on ascreen display, based upon reverse the vector transform. In addition,the resultant location in the standard coordinate space can be used toperform a lookup function within the 3-D database in order to producethe name of the anatomical structure at the corresponding location.Finally, the anatomical boundaries of the structure selected within thesubject's brain can be drawn and labeled as a contour map surroundingall voxels included within the structure, or having a thresholdprobability of being within the structure.

[0616] 7. Summary of Scanning Scanning Protocol

[0617] In this section, an exemplary scanning protocol is provided. Itis pointed out that this protocol is for illustration purposes and maybe modified as has been described in the other sections. It is alsopointed out that aspects of this protocol are directed to performing afMRI scan. Modifications to the protocol are within the level of skillin the art for other brain scanning methodologies.

[0618] After pre-scanning training has been performed, subjects arefirst placed in the scanner, and a series of scans take place over aperiod of minutes or hours.

[0619] T1-weighted saggittal localization scans are conducted tolocalize the brain precisely and achieve registration.

[0620] T1-weighted anatomical scans are also conducted to preciselyimage the brain and central nervous system

[0621] Functional scan(s) may then be performed to localize the regionsof interest. During these scans, the subject may be asked to perform atask alternating with rest periods (with each typically lasting about 30s). After this has been repeated 3-20 times, the average activity may becomputed for each voxel within the brain or other body zone in order todetermine the region(s) of interest as described above. During thisprocess, the subject observes images of the activity pattern withintheir brain so that they learn what the activation achieved by abehavior in a particular region looks like, and are encouraged by theirsuccess.

[0622] Initial training scanning is then performed to train the subjectin how to control a brain region. The subject can be asked to control aregion of the brain that is ‘easier’ to control than the ultimatetraining target so that they learn how to accomplish this and buildconfidence. In one embodiment, subjects are asked to alternativelyactivate and inactivate their functionally defined primary motor cortexdigit representation of one hand by imagined hand movement. The subjectslearn how to control this brain region and are rewarded for theircorrect performance.

[0623] The subject may be given a ‘control task’ which is identical tothe task described below, except that the information presented to thesubject does not give accurate information about the state of activationof their brain. The information presented comes from another(pre-recorded) subject, from a different brain region than the one beingconsidered, from an earlier time, or a combination. In one embodiment,the subjects may be given ‘sham feedback’ which they are told comes fromthe region of interest the second before, but actually comes fromanother brain region 30-60 s before. This allows the clear determinationthat subjects are using the information being presented to them tocontrol their brain activation (in comparison with this control casewhere they are not).

[0624] The subjects may be given multiple training periods of manytrials or continuous training. The subjects are shown the screensdescribed above, and asked to perform many trials at the times cued. Ineach trial, the subject alternated between performing the desired taskand resting or performing a different task. The subject is instructed toachieve the desired pattern of brain activation. In one embodiment, thisdesired pattern is an increase in activation in a defined brain regionduring the task period compared with the control period. As the subjectsprogress through the trials, in one embodiment an adaptive trackingprocedure is used to aid in their training. This procedure sets a targetlevel of activation for each trial based upon the level achieved inrecent trials (using a psychophysical 3 up, one down procedure). As thesubject does better, the trials become more challenging. If the subjectbegins to make errors, the trails become easier. The subject is givenboth continuous immediate information about the level of activation inthe relevant brain region, as well as information about their behavioralperformance. This training takes place either using the alternatingmethodology described, or with the subject's objective being acontinuous increase in activation of the target region, or replicationof the intended pattern.

[0625] The subjects are then given test periods to simulate beingoutside of the scanner. On certain trials, or periods of trials,subjects are not provided with information about the level of brainactivity, and they are tested to determine whether they are nonethelessable to produce the desired modulations. This simulates the situationthat the subject will encounter in controlling their brain activationstate when no longer in the scanner, and allows the evaluation of theirsuccess.

[0626] 8. Scanning Parameters

[0627] For fMRI, an example of scanning parameters that may be used isas follows. It is noted that one of ordinary skill will know how toperform fMRI and thus will know how to deviate as necessary from theseparameters.

[0628] Scanner fields can range from 0.1-10 Tesla or more. Scan volumescan range from 1 mm to 40 cm, and can be divided into voxels with edgesizes from 1 micron to 20 cm. Scan repeat rates can be 0.01 to 1000 Hz.TE can range from 1-1000 ms, and TR can range from 1-4000 ms.

[0629] 9 . Contrast Agents

[0630] It is noted that contrast agents may be optionally used incombination with fMRI for physiological signal measurement whenperforming the various methods of the present invention. By usingcontrast agents to assist brain scanning, it may be possible to achievelarger and more reliable activation measurements than using traditionBOLD signals which rely on endogenous contrast particularly as providedby hemoglobin. Examples of exogenous contrast agents that may be used inconjunction with the methods of the present invention include, but arenot limited to the contrast agents disclosed in U.S. Pat. No. 6,321,105.

[0631] 10. Background Conditions

[0632] Background conditions for training and measurement are used toset the ‘baseline’ level of a localized brain region's activation, oranother activity metric. Further measurements can be made in comparisonto this baseline. For example, a subject might be trained to increasethe level of activation of a localized brain region above a baselinelevel, and that baseline level might be determined by the activation ofthat region when the subject is resting and not performing a task. If adifferent baseline level was chosen, such as the level when the subjectperformed an alternative task, then the increase above this alternativebaseline level would be different. Frequently, the activity patternmeasure of interest is the difference in activity between a task stateand a baseline level measured for a background condition. Therefore, itis important to select an appropriate background condition.

[0633] As was described previously, the simplest background condition istypically a rest condition during which the subject is not explicitlyinstructed to perceived particular stimuli or perform particularbehaviors. However, there are circumstances and brain regions for which‘rest’ can still produce significant levels of activation. For example,if at ‘rest’ the subject tends to engage in cognitive activities such asinternal dialog or other types of thoughts, there can be activation ofcertain brain regions associated with these cognitive activities, suchas in the frontal lobes.

[0634] More complex background conditions are designed to selectivelydeactivate a region of interest, or to activate other regions than theregion of interest. For example, a background condition for a verbalmental rehearsal task is the task of imagining mental images in theabsence of internal verbalization. This background condition may lead toa lower or different pattern of activation in the region of interest,such as in the region responsible for verbal mental rehearsal. Thisbackground condition may also lead to an increase in activation in otherregions, such as occipital and frontal regions responsible for internalvisualization. Other background conditions include tasks that willinhibit subjects from engaging excessively in unrelated thoughts, suchas a simple reaction time task or a task require select which stimuluswas presented of several possibilities. In some instances a backgroundcondition to measure a truly low level of activity could be one of thevarious states of sleep such as slow wave or REM sleep, anesthesia, orother reduced level of awareness.

[0635] 11. Head Motion Stabilization

[0636] For many of the brain scanning technologies, it is important forthe subject's head to be kept stationary. This becomes an issue when thesubject is trained for an extended period of time. Accordingly, thepresent invention also relates to devices reduce head movement. Movementcancellation software and technologies may allow less restrained headmovement or free head movement during measurement using this invention.

[0637] In one embodiment, the subject is placed within a head restraintsystem similar to the type used following cervical spinal injury. Therestraint system may be anchored or placed in such a way as to ensurestability, minimize motion, and allow reproducible placement of the headin space within the scanner on successive occasions. The restraintsystem preferably is able to conform to a shape of the head and neck ofthe subject and may include adjustable straps to hold the head securelywithin the device. The materials used may be semi-rigid or a combinationof hard materials coated with softer material to make them comfortable,with all materials being scanning transparent.

[0638] In another embodiment, a custom-fitted head mold is provided tohold the head of the subject stationary. This mold is preferablyremoveably attachable to the scanner so that the mold may be immobilizedrelative to the scanner. The mold may be created through injectionmolding using a lightweight, largely rigid yet somewhat soft, andscanning-transparent material such as styrofoam to form a mold shaped tofit all or part of the subject's head, neck, and upper torso.Optionally, the subject's head motion may be additionally stabilizedusing a bite bar that is placed to allow the subject to embed his/herteach within the material and thereby maintain a fixed position.

[0639] For some applications, such as fMRI, it is desirable to preciselyposition the subject's head, for example relative to the scanner scanneror head coil. This positioning of the head may be accomplished byplacing the subject in the scanner so as to precisely locate points onthe head by matching localization points with physically constant orprecisely adjustable locations attached to the scanner or head coil. Inone variation, large plastic or other screws are threaded through holesin the apparatus holding the subject and adjacent to the head may beused. These screws may be screwed in until they just touch the head ofthe subject, with the number of turns providing a precise a reproduciblemeasure of the location of the point on the head. The screws can also beformed with soft pads attached to their ends that serve to restrainmotion of the head. Conventional neurological ‘halos’ can be adapted tothis purpose.

[0640]FIG. 13 shows an embodiment of head motion restraint for thesubject. The subject 14000, is placed within a rigid structure 14010that may be positioned within the measurement apparatus, such as an fMRIscanner. The rigid structure 14010 may serve be function of being an RFreceiver coil apparatus The head of the subject is immobilized in aconformal head mold 14020 that may be selected from a pre-existingstock, may be custom fitted for the subject, or may be injection moldedor otherwise fashioned to be in the shape to fit around a portion of thesubject's head. Localization points on the subject 14030 may be used toensure constant placement within the apparatus. These points may bematched up with the ends of either fixed or adjustable positioningmembers 14040 that are attached to the rigid structure. The positions ofthese positioning members may be reproducible across scanning sessions.By maintaining contact between the localization points 14030 and thepositioning members 14040, the position of the subject's head within thescanner may be held constant. The positioning members may be adjustablein position with respect to the rigid structure 14010. For example, thepositioning members may be threaded screws that fit through holes 14050in the rigid structure and have screw heads 14060 that allow theirposition to be adjusted. The screw threads and position of the screwheads may be calibrated and marked so that a repeatable depth of thescrew may be achieved on successive instances. More sophisticatedpositioning means be used for the positioning members, such asmicromanipulators, for example those manufactured by Kopf, Inc. orNarishige, Inc. Any number of positioning members 14040 may be used suchas 1,2,3,4,6,8,10 or more. In addition, the positioning members may beplaced on any position on the rigid structure 14010 that will allow themto contact a portion of the body of the subject, such as the top,bottom, sides, front and back of the head. The rigid structure 14010 mayalso correspond to a neurological or neurosurgical ‘halo’, or to astructure adapted from a halo for the present purpose by attachment toan MRI RF receiver coil or other element that can be preciselypositioned within a measurement apparatus such as an MRI scanner.

[0641] 12. Cardiac and Respiratory Gating

[0642] Some portions of the brain undergo significant movement as aresult of the cardiac cycle as well as respiration, and these movementsintroduce noise into physiological signals measured from thecorresponding scan volume voxels. The present invention can be used incombination with techniques that decrease the impact on measuredphysiological data of physiologically-based motion such as cardiacmotion and respiratory motion. One technology that may be used todecrease the observed motion of certain brain regions is cardiac gating.Brain measurement times are triggered by measurements of the timing orphase of the cardiac rhythm cycle so that, on average, successive brainmeasurements are taken at substantially the same point in the cycle withbrain regions in substantially the same position. For instance, thestart of each cardiac cycle is detected using an EKG or pulsoxymetrydevice, and this time is used to trigger the presentation of an MRI RFpulse sequence and ensuing measurements.

[0643] Another technology that may be used to decrease the observedmotion of certain brain regions is respiratory gating. Brain measurementtimes are triggered by measurements of the timing or phase of therespiratory rhythm cycle so that, on average, successive brainmeasurements are taken at substantially the same point in the cycle withbrain regions in substantially the same position. For instance, thestart of each respiratory cycle is detected using a pulsoxymetry device,and this time is used to trigger the presentation of an MRI RF pulsesequence and ensuing measurements.

[0644] 13. Measurement of Activity

[0645] This invention may be used in conjunction with a variety of meansfor measuring physiological activity from a subject. Examples ofmeasurement technologies include, but are not limited to, functionalmagnetic resonance imaging (fMRI), PET, SPECT, magnetic resonanceangiography (MRA), diffusion tensor imaging (DTI), trans-cranialultrasound and trans-cranial doppler shift ultrasound. It is anticipatedthat future technologies may be developed that also allow for themeasurement of activity from localized brain regions, preferably insubstantially real time. Once developed, these technologies may also beused with the current invention. These measurement techniques may alsobe used in combination, and in combination with other measurementtechniques such as EEG, EKG, neuronal recording, local field potentialrecording, ultrasound, oximetry, peripheral pulsoximetry, near infraredspectroscopy, blood pressure recording, impedence measurements,measurements of central or peripheral reflexes, measurements of bloodgases or chemical composition, measurements of temperature, measurementsof emitted radiation, measurements of absorbed radiation,spectrophotometric measurements, measurements of central and peripheralreflexes, and anatomical methods including X-Ray/CT, ultrasound andothers.

[0646] Any localized region within the brain, nervous system, or otherparts of the body that is measured using physiological monitoringequipment as described (or other physiological monitoring equipment thatmay be devised) may be used as the region of interest of this method.For example, if measurement equipment is used for the monitoring ofactivity in a portion of the peripheral nervous system, such as aperipheral ganglion, then subjects may be trained in the regulation ofactivity of that peripheral ganglion. In addition, this invention may beused to monitor the blood, blood volume, blood oxygenation level, andblood flow in the vasculature of the brain and other bodily areas, whichmay serve as regions of interest.

[0647] 14. Behavioral Training

[0648] Using this invention, subjects may be trained in a variety oftasks. Training corresponds to performing a task with the intent toimprove at a desired outcome, and is typically repeated. Tasks mayinclude covert behavioral tasks in which a subject performs a cognitiveor mental activity such as imagining a movement in order to activate abrain region, or overt behavioral tasks in which a subject performs aphysically observable action such as making a prescribed movement orresponding to a question. In either case, the task may lead to changesin the activity of the brain of the subject, and these changes may bemeasured as provided for in this invention. Overt and covert tasks maybe performed separately, or substantially concurrently.

[0649] One example of behavioral training is covert training of asubject to activate a brain region of interest. In this example, thesubject may be provided with information about the level of activity ina brain region of interest, such as an activity map including theregion, or an activity metric that measures the activity in the regionof interest. This training may be with the intent of increasing theactivity in the region of interest, decreasing it, changing its pattern,or altering it in other ways as measured by the activity pattern metricsdescribed in Examples section 1. The subject may also be presented withstimuli, which may additionally serve to activate a brain region ofinterest. The subject may also be presented with performance informationindicating his or her level of performance at the task being performed.The subject may monitor these types of measured information, stimuli,and performance information, and may respond to them. One response ofthe subject may be to select or modify a cognitive strategy that thesubject uses to activate the brain region. For example, if the subjectis performing the covert task of imagining a given hand movement in anattempt to activate the motor cortex, the subject may observe that oneparticular imagined hand movement is more effective at activating themotor cortex than another particular imagined hand movement. The subjectmay then select the more effective movement for use in future trials.This monitoring of information and response may take place incombination with performing training. While the results of a covert taskmay be observed using physiological measurement equipment, they are notobservable in the sense of producing an overt, physically observable,visibly viewable action of the subject.

[0650] Another example of behavioral training is overt training of asubject to perform a physically observable, overt task. The subject mayengage in overt tasks such as psychological, learning, motor, orpsychophysical tasks. These may include such as things as making acomputer selection of which of two stimuli presented has a particularfeature, or making a prescribed motion, or answering a stated question.The subject may additionally be given performance information regardingtheir performance at these covert tasks, such as whether they performedtasks correctly or incorrectly. The performance of covert tasks may takeplace substantially concurrently with overt tasks. For example, thesubject may be instructed to make selections between different stimulior to perform particular movements while the subject also attempts toincrease the level of activation in a brain region of interest.

[0651] It will be apparent to those skilled in the art that variousmodifications and variations can be made to the methods, software andsystems of the present invention. The foregoing examples and figures arepresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formsdisclosed. Many modifications and variations will be apparent topractitioners skilled in this art and are intended to fall within thescope of the invention.

[0652] All publications and patent applications cited in thisspecification are herein incorporated by reference as if each individualpublication or patent application were specifically and individuallyindicated to be incorporated by reference. The citation of anypublication is for its disclosure prior to the filing date and shouldnot be construed as an admission that the present invention is notentitled to antedate such publication by virtue of prior invention.

What is claimed is:
 1. Computer executable software and device for guiding brain activity training comprising: logic which takes data corresponding to activity measurements of one or more internal voxels of a brain and determines one or more members of the group consisting of: a) what next stimulus to communicate to the subject, b) what next behavior to instruct the subject to perform, c) when a subject is to be exposed to a next stimulus, d) when the subject is to perform a next behavior, e) one or more activity metrics computed from the measured activity, f) a spatial pattern computed from the measured activity, g) a location of a region of interest computed from the measured activity, h) performance targets that a subject is to achieve computed from the measured activity, i) a performance measure of a subject's success computed from the measured activity, j) a subject's position relative to an activity measurement instrument; and logic for communicating information based on the determinations to the subject in substantially real time relative to when the activity is measured.
 2. The software and device according to claim 1 wherein measuring brain activity is performed by fMRI.
 3. The software and device according to claim 1 wherein the determinations are made in less than 10 seconds relative to when the activity is measured.
 4. The software and device according to claim 1 wherein the determinations are made in less than 1 second relative to when the activity is measured.
 5. The software and device according to claim 1 wherein the determinations are made in less than 0.5 second relative to when the activity is measured.
 6. The software and device according to claim 1 wherein the information is determined while the instrument used for measurement remains positioned about the subject.
 7. The software and device according to claim 1 wherein the activity measurements are made using a device capable of taking measurements from one or more internal voxels without substantial contamination of the measurements by activity from regions intervening between the internal voxels being measured and where the measurement apparatus collects the data.
 8. The software and device according to claim 1 wherein measurements are made from at least 100 separate internal voxels, and these measurements are made at a rate of at least once every five seconds.
 9. The software and device according to claim 1 wherein measurements are made from a set of separate internal voxels corresponding to a scan volume including the entire brain.
 10. The software and device according to claim 1 wherein the size of the internal voxels have a total three dimensional volume of 5×5×5 cm or less.
 11. The software and device according to claim 1 wherein the size of the internal voxels have a total three dimensional volume of 1×1×1 cm or less.
 12. The software and device according to claim 1 wherein the software further comprises logic for selecting one or more of the internal voxels to correspond to a region of interest for the subject and using the selected internal voxels of the region of interest to make the one or more determinations.
 13. The software and device according to claim 1 wherein the information is communicated by a manner selected from the group consisting of providing audio to the subject, providing tactile stimuli to the subject, providing a smell to the subject, displaying an image to the subject.
 14. The software and device according to claim 1 wherein the information communicated is an instruction to the subject.
 15. The software and device according to claim 14 wherein the instruction is a text or iconic indication denoting an action that a subject is to perform.
 16. The software and device according to claim 14 wherein the instruction identifies a task to be performed by the subject.
 17. The software and device according to claim 14 wherein the instruction is determined by computer executable logic.
 18. The software and device according to claim 17 wherein the instruction communicated is selected from a set of instructions stored in memory, the selection being based upon the brain activity measured.
 19. The software and device according to claim 1 wherein some of the information communicated to the subject is material to be learned.
 20. A method comprising: (a) measuring activity of one or more internal voxels of a brain; (b) communicating instructions to a subject derived from that measured activity in substantially real time relative to when the behavior is performed; and (c) having the subject perform a behavior in response to receiving the instructions.
 21. A method according to claim 20 wherein measuring brain activity is performed by fMRI.
 22. A method according to claim 20 wherein measurements are made from at least 100 separate voxels.
 23. A method according to claim 20 wherein the instructions are derived through a computer executable logic process of selecting from a set of possible instructions based upon the brain activity measured.
 24. A computer assisted method comprising: measuring activity of one or more interior volumes of a brain; employing computer executable logic that takes the measured brain activity and determines information to communicate to the subject; and communicating the determined information to the subject; wherein the determined information is communicated to the subject in substantially real time relative to when the activity is measured.
 25. A method according to claim 24, wherein computer executable logic is emplyed to cause the information to be communicated to the subject.
 26. Computer executable software, the software comprising: logic for taking activity measurements of one or more localized brain regions as a behavior is performed; and logic for communicating information to the subject based on the measured brain activity in substantially real time relative to when the behavior is performed; wherein the logic takes new activity measurements as they are received and communicates new information based on the new activity measurements. 